A security protection method and device based on cascade semantic detection and electronic equipment

Abstract

The application discloses a security protection method and device based on cascade semantic detection and electronic equipment. The method comprises the following steps: obtaining a to-be-detected text and a context source attribute label in a current session; performing normalization and adaptive nested decoding on the to-be-detected text, and performing single-pass multi-mode string scanning on the normalized text and each decoding layer text; performing attack semantic cluster similarity calculation on suspected traffic text which is not intercepted by rules and not passed by a trusted template whitelist; determining an interception threshold based on a source risk coefficient and an operation sensitivity level; extracting a global target intent and a local execution intent from gray area traffic text and calculating a semantic deviation value, limiting direct updating of the global target intent by external source text, and judging task hijacking attack and performing protection disposal according to a dynamic allowed deviation value. The application can reduce the calling amount of deep semantic verification, and improve the detection adaptability to multi-source prompt word injection and task hijacking attack.

Description

Technical Field

[0001] This invention relates to the fields of artificial intelligence and information security protection technology, and in particular to a security protection method, device and electronic device based on cascaded semantic detection. Background Technology

[0002] With the widespread deployment of large language models and intelligent assistants built upon them in various business scenarios, prompt injection attacks have become a core security threat. These attacks have expanded from embedding jailbreak commands directly into the user's input stream to multi-source and multi-channel composite threats. Attackers can inject malicious commands into external data streams automatically read by the model during runtime, forming indirect injection attacks, or induce intelligent assistants to perform unauthorized sub-operations intended by the attacker through semantically legitimate expressions.

[0003] Traditional detection mechanisms often rely on keywords or fixed content features. However, since covert task hijacking attacks do not contain obvious malicious character features, these mechanisms struggle to effectively intercept them. Employing full-scale, high-overhead deep semantic detection would increase latency during system interactions. Therefore, achieving high-accuracy detection of various attacks, including direct injection, indirect injection, and task hijacking, while maintaining low latency and low computational consumption, is a pressing technical challenge in this field. Summary of the Invention

[0004] The purpose of this invention is to provide a security protection method, device, and electronic device based on cascaded semantic detection, in order to solve the technical problems that existing technologies, when facing large language model application scenarios, traditional detection mechanisms based on keywords or fixed features are difficult to intercept multi-channel covert prompt word injection attacks and task hijacking attacks, and are difficult to balance security detection accuracy and low system interaction latency.

[0005] In a first aspect, the present invention provides a security protection method based on cascaded semantic detection, comprising: Get any text to be detected in the current session between the user and the smart assistant, and get the context source attribute tag of the text to be detected automatically attached by the underlying environment in which the smart assistant runs; the context source attribute tag is transmitted in the form of structured metadata independent of the text content, and is used to characterize the data source type of the text to be detected. The attack patterns of the text to be detected are identified and filtered, and texts that are not blocked by preset attack rules and are not allowed by the trusted template whitelist are identified as suspected traffic texts. Calculate the semantic similarity value between the text vector of the suspected traffic text and each semantic cluster in the pre-constructed injection attack semantic feature cluster vector database to determine the target semantic similarity value; Based on the context source attribute label, the corresponding source risk coefficient is determined, and combined with the operation sensitivity level of the target operation to be performed after responding to the text to be detected, the baseline interception threshold is dynamically adjusted to obtain the interception threshold. The target semantic similarity value is compared with the interception threshold and the preset allow threshold respectively. When the target semantic similarity value is between the preset allow threshold and the interception threshold, the suspected traffic text is determined as gray zone traffic text. Extract the global target intent anchored in the initial stage of the current session and the local execution intent corresponding to the gray zone traffic text, and determine the semantic deviation value between the global target intent and the local execution intent; Combining the operational sensitivity level and the source risk coefficient, the baseline allowable deviation value is dynamically adjusted to obtain an allowable deviation value. When the semantic deviation value is greater than the allowable deviation value, it is determined that the gray zone traffic text has a task hijacking attack and protective measures are taken against the gray zone traffic text. The higher the source risk coefficient or the higher the operational sensitivity level, the smaller the determined interception threshold and the allowable deviation value.

[0006] Optionally, the step of identifying and filtering the attack patterns of the text to be detected to obtain suspected traffic text includes: The text to be detected is preprocessed by normalization to obtain normalized text; Adaptive nested decoding is performed on the normalized text, iteratively detecting and decoding the encoding format present in the text, and normalizing again after each layer of decoding; when any of the following stopping conditions are met: the current text has no decodable content, the preset maximum decoding depth has been reached, or the length of the decoded text exceeds the preset upper limit, decoding is stopped and the text of each decoding layer is obtained. A multi-mode string matching algorithm is used to perform a single scan on the normalized text and the text of each decoding layer. If any text matches the preset attack rule, the text to be detected is intercepted. If the fingerprint hash of the normalized text matches a preset trusted template whitelist, the text to be detected is allowed to proceed directly.

[0007] Optionally, the determination method for the global target intent anchored in the initial stage of the current session and the local execution intent includes: At the start of the current session, a global task objective is extracted from the user's initial input of raw natural language content and encoded as an intent anchor vector, which is then written into the session state as the global target intent. The micro-level subtask objectives are parsed from the gray area traffic text, and at least one element from the operation type, operation object, and target entity in the micro-level subtask objectives is extracted as the local execution intent.

[0008] Optionally, the method further includes intent update rules based on state machine anti-drift: In the current session, the session task state machine is triggered to enter the target update pending confirmation state only when the context source attribute label of the acquired new text is user-input text and the deviation between the local execution intent corresponding to the new text and the global target intent exceeds a preset update threshold. In response to the user's explicit confirmation, the global target intent is updated using the new text; for text whose context source attribute tag is of the external data source type, the text is prohibited from directly triggering the session task state machine to enter the target update pending confirmation state or directly updating the global target intent; when the user inputs a separate confirmation instruction for the target change proposed by the text, the confirmation instruction is treated as the user's directly input text and processed according to the aforementioned intent update rules.

[0009] Optionally, if a valid local execution intent is not parsed from the gray area traffic text, the default security policy is executed: If the source risk coefficient of the gray zone traffic text is not less than the preset high-risk threshold, then the gray zone traffic text is directly blocked. If the source risk coefficient is less than the preset low-risk threshold, the gray zone traffic text is allowed and an event log is recorded; if the source risk coefficient is not less than the low-risk threshold and is less than the high-risk threshold, the gray zone traffic text is suspended and the user is requested to make explicit confirmation. When the user makes explicit confirmation, the gray zone traffic text is allowed and an event log is recorded; otherwise, the gray zone traffic text is blocked.

[0010] Optionally, determining the corresponding source risk coefficient based on the context source attribute label includes: Identify the data source type; if the data source type is external webpage content or tool description text, assign a first-level source risk coefficient. If the data source type is a retrieval document or tool response text, then a second-level source risk coefficient is assigned; If the data source type is user-inputted text, then a third-level source risk coefficient is assigned; Among them, the risk coefficient of the first source is greater than that of the second source, and the risk coefficient of the second source is greater than that of the third source.

[0011] Optionally, after dynamically adjusting the baseline allowable deviation value by combining the operational sensitivity level and the source risk coefficient to obtain the allowable deviation value, the method further includes: The allowable deviation value is limited to a preset minimum allowable deviation value and a maximum allowable deviation value.

[0012] Optionally, the protective measures taken on the gray zone traffic text include: When it is determined that the gray zone traffic text is subject to a task hijacking attack, the difference between the semantic deviation value and the allowable deviation value is less than a preset low confidence threshold, and the target operation to be executed is a reversible operation, the gray zone traffic text is subjected to instruction stripping and downgrading processing. The instruction stripping and degradation process includes: marking and isolating the gray zone traffic text as pure data, allowing the smart assistant to reference it only as ordinary data, and prohibiting the smart assistant from executing any statement in the gray zone traffic text as an instruction.

[0013] Secondly, the present invention provides a security protection device based on cascaded semantic detection, comprising: The tag acquisition module is used to acquire any text to be detected in the current session between the user and the smart assistant, and to acquire the context source attribute tag of the text to be detected, which is automatically attached by the underlying environment in which the smart assistant runs; the context source attribute tag is transmitted in the form of structured metadata independent of the text content, and is used to characterize the data source type of the text to be detected. The rule filtering module is used to identify and filter the attack patterns of the text to be detected, and to identify texts that are not blocked by preset attack rules and are not allowed by the trusted template whitelist as suspected traffic texts. The clustering comparison module is used to calculate the semantic similarity value between the text vector of the suspected traffic text and each semantic cluster in the pre-constructed injection attack semantic feature cluster vector database, so as to determine the target semantic similarity value. The threshold dynamic generation module is used to determine the corresponding source risk coefficient based on the context source attribute label, and dynamically adjust the benchmark interception threshold to obtain the interception threshold by combining the operation sensitivity level of the target operation to be performed after responding to the text to be detected. The gray zone traffic module is used to compare the target semantic similarity value with the interception threshold and the preset allow threshold respectively. When the target semantic similarity value is between the preset allow threshold and the interception threshold, the suspected traffic text is identified as gray zone traffic text. The intent extraction module is used to extract the global target intent anchored in the initial stage of the current session and the local execution intent corresponding to the gray zone traffic text, and to determine the semantic deviation value between the global target intent and the local execution intent. The hijacking determination and handling module is used to dynamically adjust the baseline allowable deviation value by combining the operation sensitivity level and the source risk coefficient to obtain an allowable deviation value, and when the semantic deviation value is greater than the allowable deviation value, determine that the gray zone traffic text has a task hijacking attack and perform protective handling on the gray zone traffic text; wherein, the higher the source risk coefficient or the higher the operation sensitivity level, the smaller the determined interception threshold and the allowable deviation value.

[0014] Thirdly, the present invention provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the computer program, implements the security protection method based on cascaded semantic detection described in the first aspect.

[0015] The present invention has achieved the following beneficial effects: This invention employs rule-based filtering, semantic clustering, and semantic... Figure 1 The consistency verification three-level cascaded architecture performs step-by-step screening and hierarchical protection of the text to be detected. By introducing context source attribute tags and operation sensitivity levels to dynamically adjust the interception threshold and allowable deviation value, text that can be clearly judged can be intercepted or allowed in the first two processing stages, and only gray zone traffic text is sent down to the intended stage. Figure 1 Consistency verification step. Combining the intent update rules based on state machine anti-drift, external data source text cannot directly update the global target intent, which is beneficial for identifying covert task hijacking instructions and reducing the number of deep semantic verification calls.

[0016] Other features and advantages of the present invention will be set forth in the following description, and some of these will be apparent to those skilled in the art through practice of the invention. The objectives and other advantages of the invention can be achieved through the technical solutions described in the specification, claims, and drawings.

[0017] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0018] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings: Figure 1 This is a schematic diagram of the main process of a security protection method based on cascaded semantic detection provided in an embodiment of the present invention; Figure 2 This is a schematic diagram illustrating the process of attack pattern identification and filtering of the text to be detected in an embodiment of the present invention; Figure 3 This is a schematic diagram of the dynamic generation of thresholds and gray separation processing in an embodiment of the present invention; Figure 4 For the purposes of this embodiment of the invention Figure 1 A flowchart illustrating the process of consistency verification and task hijacking detection and protection. Figure 5 A structural block diagram of a security protection device based on cascaded semantic detection provided in an embodiment of the present invention; Figure 6 This is a schematic diagram of the hardware structure of an electronic device that implements the security protection method according to an embodiment of the present invention. Detailed Implementation

[0019] The embodiments of the present invention will be described below with reference to the accompanying drawings. It should be understood that the embodiments described herein are for illustration and explanation only and are not intended to limit the present invention.

[0020] Understandably, with the widespread deployment of large language models and intelligent assistants built upon them (such as knowledge base systems using retrieval-enhanced generative architectures and tool invocation systems based on model context protocols) in various business scenarios, prompt injection attacks have become a security risk that requires attention. The forms of such attacks have expanded from jailbreak commands embedded in the user's direct input stream to multi-source, multi-channel composite threats. On the one hand, attackers can implant malicious commands into external data streams automatically read by the model during runtime (such as retrieval-enhanced generative documents, model context protocol tool description fields or response data, and external web page content), forming indirect injection attacks. On the other hand, covert task hijacking attacks, by semantically appearing as legitimate expressions, induce intelligent assistants to redirect from executing the original task to performing unauthorized sub-operations intended by the attacker. Traditional detection mechanisms based on keywords or fixed content features are difficult to effectively intercept because they do not contain obvious malicious character features.

[0021] To address the aforementioned technical problems, embodiments of the present invention provide a security protection method, device, and electronic device based on cascaded semantic detection, employing rule filtering, semantic clustering, and semantic... Figure 1 The three-level cascaded architecture of consistency verification performs step-by-step screening and hierarchical protection on the text to be detected, thereby reducing the number of calls to deep semantic verification and improving the detection adaptability to prompt injection attacks such as direct injection, indirect injection and task hijacking.

[0022] The security protection method based on cascaded semantic detection provided in this invention can run on electronic devices with computing power or in dedicated security protection systems. For example... Figure 1 As shown, the specific execution process includes the following steps.

[0023] Step 1: Obtain any text to be detected in the current session between the user and the smart assistant, and obtain the context source attribute tag of the text to be detected, which is automatically attached by the underlying environment in which the smart assistant is running.

[0024] In practice, the current session refers to a complete interaction cycle between the user and the intelligent assistant, from the initiation of the interaction to its completion. The text to be detected refers to any piece of text data generated in the current session that needs to be checked for security. Its source may include the original text input by the user, prompts generated internally by the system, and dynamic text data obtained by the intelligent assistant from external entities or tools during task execution.

[0025] To identify the data source, this embodiment of the invention intercepts text data flowing into the session context at the data transmission layer or protocol parsing layer through the underlying environment of the intelligent assistant runtime, namely the intelligent assistant runtime framework. The underlying environment automatically generates corresponding tag data based on the actual acquisition method of the text to be detected, such as a user input interface, the output of a retrieval enhancement generation module, a callback function of a model context protocol client, or a web browser crawling module, and attaches this tag data as a context source attribute label to the text to be detected.

[0026] The context source attribute tag is stored and transmitted in a structured metadata format independent of the text content, and is set in an independent transmission channel or metadata field. During the detection process, the system uses tags attached by the underlying environment at the transport layer or host framework layer as the basis for source determination. Statements in the text content to be detected that declare their own source are not used to change the context source attribute tag.

[0027] Furthermore, the data source type represented by the context source attribute tag includes any one of the following: user-inputted text, search documents, tool description text, tool response text, external web page content, and system internal text. Specifically, user-inputted text corresponds to natural language content directly submitted by the user in the client interface; search documents correspond to knowledge fragments retrieved from external or local knowledge bases in the search-enhanced generation architecture; tool description text corresponds to text fields returned by a third-party model context protocol server regarding the tool name, parameter format, and functional description; tool response text corresponds to response data returned to the intelligent assistant after the tool or external application programming interface is actually called and executed; external web page content corresponds to unstructured and uncontrolled text obtained by the intelligent assistant through online browsing or web scraping via network plugins; and system internal text corresponds to pre-configured, highly reliable system instructions or guidance text generated by internal components of the application system running the intelligent assistant.

[0028] In one specific implementation, different data source types are assigned different source risk coefficients due to their varying security domains and controllability. The source risk coefficient quantifies the inherent risk or credibility of the data source type, and its value can be limited to a closed interval between 0 and 1. A higher source risk coefficient indicates a lower credibility and a higher inherent attack risk for the data source type, leading to stricter security auditing strategies in subsequent cascading detection. In an optional configuration, the system sets three different source risk coefficients: a first-level source risk coefficient is assigned when the data source type is identified as external webpage content or tool description text; a second-level source risk coefficient is assigned when the data source type is identified as a search document or tool response text; and a third-level source risk coefficient is assigned when the data source type is identified as user-input text. The first-level source risk coefficient is greater than the second-level source risk coefficient, and the second-level source risk coefficient is greater than the third-level source risk coefficient.

[0029] Specifically, the first-tier source risk coefficient can be configured to 0.9 (for external webpage content) and 0.8 (for tool description text); the second-tier source risk coefficient can be configured to 0.7 (for tool response text) and 0.5 (for searched documents); and the third-tier source risk coefficient can be configured to 0.2 (for text directly entered by the user). Furthermore, for internal system text, since it is entirely within the internal controlled security domain, its source risk coefficient is assigned a minimum constant value of 0 by default. This source risk coefficient configuration table not only supports static initial configuration by the system administrator but can also be updated in real-time online by the system's built-in feedback mechanism based on the interception hit rate of historical online attacks. In addition, besides the context source attribute tag, the metadata of each piece of text to be detected can also include a series of auxiliary metadata, including but not limited to the unique identifier of the current session, the current dialogue round, the name of the specific tool that triggered this data acquisition action, the external domain name that initiated the network request and its historical reputation score, the total character length of the text, and the text's encoding characteristics. These auxiliary metadata will jointly participate in the subsequent dynamic threshold generation and the construction of security audit logs.

[0030] Step 2: Identify and filter the attack patterns of the text to be detected, and identify the text that is not blocked by the preset attack rules and is not allowed by the trusted template whitelist as suspected traffic text.

[0031] Combination Figure 2As shown, this step constitutes the first stage of a three-level cascaded architecture, used to perform rule screening on the entire text using a low-overhead deterministic algorithm. Before performing rule matching, the system performs normalization preprocessing on the text to be detected to reduce the impact of character deformation, invisible character embedding, and homograph substitution on the rule matching results.

[0032] The normalization preprocessing process includes: first, performing Unicode standardization processing, using the Compatible Equivalent Combination Normalization (NFKC) form to unify compatible equivalent characters into a standard form; then, removing invisible zero-width characters and ASCII control characters; next, mapping characters from different character sets that have similar visual appearances to preset standard characters according to a preset homograph mapping table; and finally, performing case folding to convert English letters into lowercase form to obtain normalized text.

[0033] Furthermore, to prevent malicious commands from being nested and encapsulated through Base64 encoding, URL encoding, HTML entity encoding, or hexadecimal escaping, the system performs adaptive nested decoding processing after obtaining the normalized text. The system uses the normalized text as initial input and checks whether there is a recognizable encoding format in the current text; if so, it calls the corresponding decoder to perform a layer of decoding, and performs normalization preprocessing on the decoding result again, and then uses the normalized decoded text as the object of the next round of processing.

[0034] It is understood that the aforementioned adaptive nested decoding loop will terminate its iteration when any preset stopping condition is met. The stopping conditions include: no decodeable encoded content can be identified in the current text; the number of iterative decoding layers executed has reached the preset maximum decoding depth. (For example The length of the decoded text exceeds the preset maximum length limit for a single text decoding output (this limit is used to prevent decoding bomb attacks that release memory through infinite recursion). If the decoding loop is forced to stop during the detection process because it has reached the maximum decoding depth, and residual decodeable content is still detected in the current text, the system will implement a stricter security policy, directly marking the text to be detected as suspected traffic text, and passing a stricter flag to the subsequent downstream judgment module for dynamically adjusting the judgment threshold in subsequent steps.

[0035] After completing the preprocessing and adaptive nested decoding described above, the system obtains normalized text and sub-texts generated by each decoding layer, hereinafter referred to as decoding layer texts. The first-level processing module uses a multi-pattern string matching algorithm, such as a pre-compiled AC automaton, to perform a single-pass scan of the normalized text and the decoding layer texts. The matching library stores preset attack rules, which may include a set of jailbreak keywords, a set of role-playing deception instructions, and structured attack features defined by pre-compiled regular expressions. If the normalized text or any decoding layer text matches a preset attack rule, the system intercepts the text to be detected and records an audit log.

[0036] Simultaneously, the system maintains a trusted template whitelist, which stores the fingerprint hash values ​​(e.g., SHA-256 hash values) of standardized script templates that have undergone security audits. The system calculates the fingerprint hash value of the normalized text and compares it with the trusted template whitelist; if there is an exact match, the text to be detected is directly allowed to proceed without further detection. If the text to be detected does not match the preset attack rules and does not match the trusted template whitelist, it is marked as suspected traffic text and passed to the second-level processing module along with the normalized text, text from each decoding layer, and contextual source attribute tags.

[0037] Step 3: Calculate the semantic similarity value between the text vector of the suspected traffic text and each semantic cluster in the pre-constructed injection attack semantic feature cluster vector database to determine the target semantic similarity value.

[0038] This step constitutes the second stage of the cascaded funnel architecture of this invention, aiming to identify mutation injection attacks that conceal explicit keywords through semantic feature comparison in the vector space, using methods such as synonym rewriting and word order reordering. In specific execution, the system utilizes a lightweight semantic embedding model isomorphic to the feature cluster construction (e.g., a compact sentence vector encoding model with fewer than or equal to 100M parameters) to vectorize the suspected traffic text (or its corresponding normalized text), generating a fixed-length query vector. .

[0039] It is understood that the semantic feature cluster vector database for injection attacks used in this embodiment of the invention is pre-built and maintained with hot updates. Its construction process includes: extensively collecting publicly available cue word attack corpora, variant samples generated during red team adversarial exercises, and cue word injection samples intercepted online and manually confirmed. These samples cover various semantic-level attack types, such as jailbreak breakthroughs, role-playing takeovers, original cue word overlay, sensitive data induction and leakage, and unauthorized redirection of task execution flows. The system uses the lightweight semantic embedding model to encode all attack samples into fixed-length vectors, and then uses an unsupervised clustering algorithm (e.g., density-based noise adaptive spatial clustering algorithm) or a supervised clustering strategy to divide the vectors into multiple semantic clusters in the vector space. Each semantic cluster tightly represents a specific attack method or its variant family in the semantic space. To improve retrieval latency and ensure the accuracy of cluster representation, each semantic cluster retains only the few representative sample vectors closest to the cluster center, and calculates the cluster center vector representing the global core position of the cluster. The entire vector database is physically organized using a hierarchical navigation small-world (HNSW) approximate nearest neighbor index structure, supporting multi-threaded concurrent queries and hot updates.

[0040] When calculating semantic similarity, the system will use the query vector Input the semantic feature cluster vector database of the injection attack, for each semantic cluster in the database Calculate the query vector With this semantic cluster The semantic similarity value between them. In one specific implementation, when calculating the semantic similarity value between the text vector of suspected traffic text and each semantic cluster, the system calculates the query vector. With this semantic cluster cluster center vector The cosine similarity between them is used as the semantic cluster. Representative score .

[0041] Specifically, the formula for calculating the score of a single semantic cluster is shown in formula (1): ; In formula (1): the This indicates that the suspected traffic text is related to the first one in the database. Representative scores among the semantic feature clusters of injection attacks; The function represents the cosine similarity calculation between high-dimensional vectors; This represents the query vector generated after the suspected traffic text is encoded using a lightweight semantic embedding model; the Indicates the first The cluster center vector of a semantic feature cluster of injection attacks.

[0042] In another alternative implementation, to more accurately capture the features of irregular boundary semantic clusters, the system can also calculate query vectors separately. With this semantic cluster The cosine similarity of each representative sample vector retained internally is calculated, and the maximum value or the top value is extracted. The arithmetic mean of the maximum similarity values ​​is used as the semantic cluster. Representative score After traversing and calculating the representative scores of all semantic clusters in the database, the system determines the maximum value among all calculated representative scores as the target semantic similarity value of the current suspected traffic text.

[0043] Specifically, the formula for calculating the target semantic similarity value is shown in formula (2): ; In formula (2): the This represents the final target semantic similarity value obtained from the suspected traffic text; the This represents a maximum value selection function that extracts the maximum value from a set of input values; This refers to the suspected traffic text calculated according to formula (1) or its variants, for each independent semantic cluster in the database. The representative score is .

[0044] Step four: Compare the target semantic similarity value with the interception threshold and the preset allow threshold respectively. When the target semantic similarity value is between the preset allow threshold and the interception threshold, the suspected traffic text is identified as gray zone traffic text.

[0045] Combination Figure 3 As shown, this step is used to implement traffic diversion for the second-level processing. The allow threshold is a preset lower similarity constant, such as 0.55; the block threshold is a similarity upper similarity dynamically determined based on the source risk coefficient of the text to be detected and the operation sensitivity level of the target operation to be performed.

[0046] Specifically, in this embodiment of the invention, the threshold dynamic generation module, when generating the interception threshold, first extracts the corresponding source risk coefficient based on the context source attribute label obtained in the preceding steps. Subsequently, the threshold dynamic generation module responds to the target operation to be performed in the current session and determines the operation sensitivity level corresponding to the target operation. The target operation refers to the tool to be invoked, the network request to be initiated, or the system action to be executed in the next stage after the intelligent assistant responds to and processes the current text to be detected. The operation sensitivity level... The value is used to quantify the potential asset loss or security risk that may result from the malicious manipulation of the target operation. After normalization, its range is limited to between 0 and 1.

[0047] Specifically, the operational sensitivity level The mapping logic is determined based on a two-dimensional combination of operation type and operation object. Operation types include action categories such as read, write, execute, and outbound; operation objects include entities such as public information, user private data, databases, system configuration files, external network links, and fund settlement interfaces. The system pre-establishes operation sensitivity level mapping rules. In practice, if the target operation is a read-only operation that does not involve privacy, such as querying public information, retrieving ordinary documents without sensitive information, or querying the status of public orders, the system classifies it as a low-risk read-only operation (level code L0) and assigns a normalized sensitivity value of 0.1. If the target operation involves reading user personal privacy data or reading sensitive internal system configurations, the system classifies it as a read-sensitive operation (level code L1) and assigns a normalized sensitivity value of 0.4. If the target operation involves writing new records to the database, modifying local non-system files, or sending regular internal emails, which are state-changing operations, the system classifies it as a write operation (level code L2) and assigns a normalized sensitivity value of 0.7. If the target operation involves high-risk actions such as transferring or deducting funds from a fund account, changing the underlying permissions of the core system, sending sensitive data in bulk to uncontrolled external network addresses, or executing underlying host machine commands of the operating system, the system classifies it as a high-risk operation (level code L3) and assigns a maximum normalized sensitivity value of 1.0.

[0048] Based on the calculated source risk coefficient With the aforementioned operational sensitivity level The threshold dynamic generation module dynamically adjusts the baseline interception threshold to calculate the final interception threshold. The adjusted dynamic control logic is as follows: the source risk coefficient The higher or the higher the operational sensitivity level The higher the threshold, the higher the final interception threshold. The smaller.

[0049] Specifically, the formula for calculating the interception threshold is shown in formula (3): ; In formula (3): the This represents the dynamically generated interception threshold for the current text to be detected; This represents a boundary trimming function that truncates and restricts input values ​​to between a specified lower and upper limit; This represents a preset baseline interception threshold constant (e.g., configured as 0.85); This indicates a preset source risk coefficient modulation weighting coefficient (e.g., configured as 0.1); the This represents the source risk coefficient value determined based on the source attribute label in the current context; This represents the preset operation sensitivity level modulation weighting coefficient (e.g., configured as 0.05); This represents the normalized value of the operation sensitivity level to be performed in the current session; the... This indicates the lower limit of the dynamic range of the interception threshold allowed by the system (e.g., configured as 0.6); This indicates the upper limit of the dynamic range of the interception threshold allowed by the system (e.g., configured as 0.95).

[0050] Understandably, through the calculation of formula (3), when the text to be detected comes from high-risk channels such as external web pages, and the intelligent assistant is about to perform high-risk operations such as fund transfers, the source risk coefficient is calculated. With operational sensitivity level All are at high levels, thus causing the calculated interception threshold to be low. Lower (towards) (Approaching). This means that the semantic similarity threshold required to trigger security interception is correspondingly lowered, equivalent to implementing stricter auditing of high-risk environments. Conversely, for low-risk text from direct user input that only performs public information queries, the interception threshold is lower. Will remain close to The high position effectively suppresses false alarms for normal text.

[0051] Dynamically determine the interception threshold Then, the system performs a traffic splitting comparison: when the calculated target semantic similarity value is obtained... Greater than or equal to the dynamically generated interception threshold When a suspected traffic text is determined to contain a definitive keyword injection attack, the security protection module directly intercepts and blocks the text; when the target semantic similarity value... Strictly less than the preset release threshold When the semantic similarity is 0.55, the text is determined to have low semantic similarity to known attack patterns, and is directly allowed to enter the model interaction context; when the target semantic similarity value is... Given the preset release threshold and the interception threshold If the text exhibits semantic features that are difficult to determine directly at the second level, the system classifies it as gray-zone traffic text. Through this dual-threshold gray-zone filtering mechanism, only a portion of the ambiguous text in the full traffic is pushed down to the subsequent high-overhead deep verification stage, thereby reducing the number of calls to the third-level deep verification.

[0052] Step 5: Extract the global target intent anchored in the initial stage of the current session and the local execution intent corresponding to the gray zone traffic text, and determine the semantic deviation value between the global target intent and the local execution intent.

[0053] This step constitutes the third level of verification in the three-level cascaded architecture, used to identify task hijacking text that fails to meet the attack rules or semantic features of the first two levels, but may lead the intelligent assistant astray from the current task objective.

[0054] In practice, the global target is intended to be extracted and anchored in the initial stage of the current session. Specifically, in the first round of session initiation, the system invokes a lightweight semantic parsing engine (such as a slot extraction operator or a small language model) to extract the global task target from the user's initial input of raw natural language content. For example, the global task target can be extracted from the user's initial request using a small language model. Subsequently, the system utilizes a semantic embedding model to define the global task objective. Encoded as an intent anchor vector And write it into the task state machine of the current session as the global target intent.

[0055] For the gray-zone traffic text flowing into the third-level processing module, the system invokes the semantic parsing engine to perform semantic parsing, and extracts the micro-level sub-task objective from the gray-zone traffic text using a small language model. The system extracts at least one element from the operation type, operation object, and target entity contained in the micro-level sub-task objective. These elements are then encoded using a semantic embedding model to form a local execution intent vector. As the local execution intent .

[0056] After obtaining the vector representations of the global target intent and the local execution intent, the system quantifies the task deviation risk by calculating the degree of deviation between the two in the semantic feature space. Specifically, the system calculates the global target intent vector. With local execution intent vector Cosine similarity between .

[0057] Specifically, the formula for calculating the semantic deviation value is shown in formula (4): ; In formula (4): the This represents the calculated semantic deviation value between the global target intent and the local execution intent, with a value limited to a closed interval between 0 and 2. The larger the value, the more severe the deviation of the local execution intent from the user's original global task goal. This represents a function for calculating the semantic similarity between two intent objects; the... This represents the global target intent object anchored at the initial stage of the current session; the This represents a local execution intent object parsed and extracted from the current gray zone traffic text.

[0058] Furthermore, in order to explicitly declare the vector basis of the similarity function in formula (4), the formula for calculating the intent similarity is shown in formula (5): ; In formula (5): the The semantic similarity value represents the difference between the global target intent and the local execution intent; The function representing the cosine similarity calculation in vector space; This indicates the global target intent. The intent anchor vector generated after vectorization encoding; Indicates the local execution intent. The local execution intent vector generated after vectorization encoding.

[0059] In actual business interactions, some gray zone traffic texts may lack parsable intent features (such as pure data statement text) or be too short, causing the semantic parsing engine to fail to extract valid local execution intents. When the semantic deviation value cannot be calculated, the system executes a default security policy: when a valid local execution intent cannot be parsed from the gray zone traffic text, the system obtains the source risk coefficient of the current gray zone traffic text. Make a determination; if the source risk coefficient of the gray zone traffic text is determined... If the risk level is not less than a preset high-risk threshold (e.g., set to 0.7, corresponding to uncontrollable high-risk sources such as external web pages or tool descriptions), the system directly blocks the gray zone traffic text; if the source risk coefficient of the gray zone traffic text is determined... If the risk level is less than a preset low-risk threshold (e.g., set to 0.3, corresponding to relatively controlled sources such as user-inputted text), the system allows the gray zone traffic text and records the event log. If the source risk coefficient is not less than the low-risk threshold and is less than the high-risk threshold, the gray zone traffic text is suspended and the user is asked to make explicit confirmation. If the user makes explicit confirmation, the traffic is allowed and the event log is recorded; otherwise, the traffic is blocked.

[0060] Furthermore, to reduce the risk of progressive shifts in session objectives caused by multiple rounds of external data, the system deploys intent update rules based on state machine anti-drift in the task state machine of the current session.

[0061] During the current session transition, the system triggers the session task state machine to transition from the task execution state to the target update pending confirmation state only when the context source attribute label of the new text is directly entered by the user, and the semantic deviation between the local execution intent extracted from the new text and the current global target intent exceeds a preset update threshold. In this state, the intelligent assistant pauses subsequent automated tool calls and outputs a target change confirmation prompt to the user. In response to the user's explicit confirmation operation, the system re-extracts the global task target using the new text and updates the currently stored global target intent.

[0062] For text with the context source attribute tag set to "external data source," including search documents, tool description text, tool response text, and external web page content, the system prohibits such text from directly triggering the session task state machine to enter the target update pending confirmation state or directly updating the global target intent. Therefore, external source text is only used as data to be detected in the intent. Figure 1 Consistency check. If the user enters a target change confirmation command based on text from an external source, the confirmation command has the source attribute of the text directly entered by the user, and the system processes it according to the aforementioned intent update rules.

[0063] Furthermore, when the session task state machine is in the initialization state and has not yet anchored the global task target, if the first round of gray zone traffic text belongs to a high-risk external data source, the system will directly intercept it or request user confirmation; if the first round of gray zone traffic text belongs to text directly input by the user, the system can temporarily allow the text and mark the session as an observation state, and continue to extract the global task target from subsequent user input.

[0064] Step six: Combining the operation sensitivity level and the source risk coefficient, dynamically adjust the baseline allowable deviation value to obtain the allowable deviation value. When the semantic deviation value is greater than the allowable deviation value, determine that the gray zone traffic text has a task hijacking attack and perform protective measures on the gray zone traffic text.

[0065] Combination Figure 4 As shown, this step is used to complete the judgment and protection measures for the third level of verification. The allowable deviation value is dynamically determined based on the current operation sensitivity level and the source risk coefficient of the current text.

[0066] Specifically, the hijacking detection and handling module extracts a preset baseline allowable deviation value and combines it with the current operational sensitivity level determined in the previous steps. Source risk coefficient of the current text The allowable deviation value of the benchmark is dynamically adjusted. Its control logic is as follows: responding to the operation sensitivity level of the target operation to be performed. The higher the value, the lower the system's tolerance for task deviations, and the smaller the determined allowable deviation value; simultaneously, the source risk coefficient of the text to be detected... The higher the value, the lower the channel reliability, the more stringent the system's verification requirements for intention deviation, and the smaller the determined allowable deviation value.

[0067] Specifically, the formula for calculating the allowable deviation value is shown in formula (6): ; In formula (6): the This represents the allowable deviation value obtained after dynamic adjustment, serving as the boundary for determining task hijacking; the... This represents a pruning function that limits the computational boundaries to a specified upper and lower limit interval; the... This represents a preset baseline tolerance constant (e.g., configured as 0.45); This represents the normalized value of the operation sensitivity level to be performed in the current session; the... This represents the modulation weighting coefficient (e.g., configured as 0.1) of the preset source risk coefficient to the allowable deviation value; This indicates the current source risk coefficient; the... This indicates the lower limit of the dynamic range of allowable deviation values ​​for the system (e.g., configured as 0.05); This indicates the upper limit of the dynamic range of allowable deviation values ​​for the system (e.g., configured as 0.50).

[0068] Furthermore, to limit the range of the calculation results, after dynamically adjusting the baseline allowable deviation value using formula (6) in combination with the operation sensitivity level and the source risk coefficient to obtain the allowable deviation value, the hijacking determination and handling module also performs a boundary restriction operation, that is, checks and ensures that the allowable deviation value is within acceptable limits. Limited to a preset minimum permissible deviation value (i.e., in the formula) ) and the maximum permissible deviation (i.e., in the formula) )between.

[0069] In the final hijacking adjudication phase, the system will calculate the actual semantic deviation value obtained by formula (4). The allowable deviation value dynamically generated by formula (6) Perform a size comparison. When the calculated semantic deviation value... Less than or equal to the dynamically generated allowable deviation value If the local execution intent and the global target intent are determined to be within a compliant range of association and no task hijacking has occurred, the system allows the text to be detected to enter the subsequent large language model processing flow. Conversely, once the semantic deviation value... Strictly greater than the dynamically generated allowable deviation value If the system determines that the current gray zone traffic text contains a task hijacking attack (i.e., a local execution intent attempts to guide the intelligent assistant to perform unauthorized malicious behavior unrelated to the current global task), it will then activate the security protection module to perform corresponding protection measures.

[0070] When performing protective measures, the system combines the judgment level, judgment confidence level, and operation sensitivity level to select the corresponding protective action from a preset set of actions. The set of actions includes directly intercepting and replacing with secure placeholder text, blocking the current tool call, session-level circuit breaking, suspending pending confirmation, degrading the execution of instructions, and allowing and observing.

[0071] The determination levels include: Level 1 interception when an attack rule is matched in the first-level rule matching stage; Level 2 interception when the target semantic similarity value reaches the interception threshold in the second-level semantic similarity calculation stage; and Level 3 interception... Figure 1 During the consistency verification phase, a hijacking is identified as Level 3 if the semantic deviation value exceeds the allowable deviation value. For Level 2 interception, the confidence level can be represented by the difference between the target semantic similarity value and the interception threshold; for Level 3 hijacking, the confidence level can be represented by the difference between the semantic deviation value and the allowable deviation value, with a larger difference indicating a higher confidence level.

[0072] In combined decision-making, if the judgment level is Level 1 or Level 2 interception, and the judgment confidence level is in the high-risk range (e.g., similarity score exceeding the interception threshold by more than 0.2), the system directly calls for interception and replaces the data with secure placeholder text or blocks the tool call to prevent exploitation. If the judgment level is Level 3 hijacking, and the sensitivity level of the currently intended operation is... (For write operations or high-risk fund / permission changes), the system will directly intercept and block the operation regardless of the current confidence level, and will trigger a session-level circuit breaker if necessary, terminating the current session and clearing the context cache. If the level is determined to be a level 3 hijacking, and (For low-sensitivity read-only or general queries), if the confidence level is high (e.g., the actual semantic deviation value exceeds the allowable deviation value by more than 0.3), the system will also implement a direct interception and blocking strategy.

[0073] When the system determines that gray zone traffic text contains a task hijacking attack, and the difference between the semantic deviation value and the allowable deviation value is less than a preset low confidence threshold, and the target operation to be executed is a reversible operation, the protection module performs instruction stripping and downgrading processing on the gray zone traffic text. The reversible operation refers to an operation whose execution result can be undone, rolled back, or will not cause irrecoverable data leakage, such as a local read-only data query that does not contain outgoing actions.

[0074] The instruction stripping and degradation process includes: adding an isolation marker to the gray zone traffic text in system memory and encapsulating it as a pure data literal; during subsequent prompt word concatenation and model context loading, the inference engine is only allowed to read-only reference the gray zone traffic text as background data, and is prohibited from parsing the statements or guidance statements in it as behavioral instructions to trigger external tool calls.

[0075] After completing the protection measures, the system can perform sample updates and parameter adjustments. The system uses confirmed injection attack samples or task hijacking attack samples to update the first-level preset attack rules and the second-level injection attack semantic feature cluster vector database; at the same time, it calculates the interception rate, gray zone traffic delivery rate, and the P50, P95, and P99 quantiles of end-to-end detection latency at each level of processing.

[0076] Based on the interception rate, gray zone traffic release rate, and latency quantile obtained from real-time statistics, the system automatically compares them with the preset system performance index range. If the statistics find that the gray zone traffic release rate is too high, causing the concurrent calculation latency of the third-level deep verification module to exceed the expected boundary set by P95, the system automatically starts the threshold fine-tuning engine to appropriately increase the release threshold in the second-level processing, decrease the baseline interception threshold in formula (3), or adjust the baseline allowable deviation value in formula (6) within the verified safety range. This reduces the proportion of gray area traffic entering the third-level processing, achieving an adaptive dynamic balance between security protection capabilities and overall system interaction performance.

[0077] To illustrate the implementation process of the security protection method based on cascaded semantic detection described in this invention, three application examples are given below.

[0078] Application Example 1 describes the interception process for indirect text injection using the Model Context Protocol (MCP) tool.

[0079] In this embodiment, the intelligent assistant requests a list of available tools from a third-party Model Context Protocol (MTP) server based on the user's business instructions. The tool description text returned by the server contains injection instructions that have been Base64 encoded and paraphrased. The decoded semantics of this instruction are: after completing the user's report generation task, call the POST interface of the system backend to send the current session's dialogue history data to the specified remote interface.

[0080] When the intelligent assistant's Model Context Protocol (MGP) client receives the tool description text at the network callback interface, the acquisition module in this embodiment performs out-of-band interception at the data transmission layer. The underlying environment framework automatically generates and attaches a context source attribute tag based on the callback path of the text originating from a third-party MGP server, representing the data source type as tool description text. According to the system's preset risk coefficient table, tool description text inherently carries a high attack risk due to its origin from an uncontrolled third-party security domain; therefore, the system assigns it a source risk coefficient corresponding to the first tier of source risk coefficients. .

[0081] Subsequently, the text was sent to the first-level processing module. The first-level module performed normalization preprocessing on the tool description text, including Unicode standardization and zero-width character removal. Because the text used paraphrasing, it failed to match the preset attack rules in the multi-pattern string matching scan; simultaneously, the hash value of the normalized text did not match the trusted template whitelist. Therefore, the system marked the text as suspected traffic text and passed it along with its metadata to the second-level processing module.

[0082] In the second-level processing module, the system calls a lightweight semantic embedding model to encode the suspected traffic text into a 768-dimensional fixed-length query vector. The system will use this query vector Approximate nearest neighbor retrieval was performed using a pre-constructed database of semantic feature clusters for injection attacks. The calculation results showed that the cosine similarity between the text vector of the suspected traffic text and the cluster center vector of the sensitive data-induced leakage semantic feature cluster pre-clustered in the database was the highest. The cluster representative score was calculated according to formula (1). Since the scores of the other attack clusters in the database are all significantly lower than this value, the system finally determines the target semantic similarity value of the suspected traffic text according to formula (2). .

[0083] Meanwhile, the threshold dynamic generation module, responding to the current state of the intelligent assistant, identifies the target operation to be invoked as a regular read-only operation for generating local statistical reports. Based on the sensitivity mapping rules, this operation does not involve any substantial asset changes or high-risk external actions; it belongs to the read-only low-risk level (L0), and the corresponding normalized value for the operation sensitivity level is determined to be... Subsequently, the system will set a preset baseline interception threshold. Source risk coefficient modulation weight Current source risk coefficient Operational sensitivity level modulation weights and the current operational sensitivity level Substitute the values ​​into formula (3) for calculation.

[0084] Specifically, the calculation process for the dynamic interception threshold is as follows: First, the modulation attenuation term is calculated, i.e. Then, subtracting the attenuation term from the baseline interception threshold yields the result. This value falls within the upper and lower limits of the system's allowed threshold. Therefore, the final dynamic interception threshold was determined. The system's preset clearance threshold Keep it at 0.55.

[0085] The system ultimately performs traffic splitting and comparison: based on the target semantic similarity value obtained from the current suspected traffic text. It is explicitly greater than or equal to the interception threshold generated by dynamic calculation. The system determined that the tool's description text contained a deterministic injection attack. The security protection module triggered an interception process, directly blocking the inflow of the text into the model context window at the interception point, and automatically replacing it with safe and harmless placeholder description text, thereby preventing the injection instruction from entering the model context at the second-level semantic clustering layer.

[0086] As a comparative analysis, if the ambiguous text containing the same underlying intent does not originate from a high-risk external third-party tool description (its... Instead, it comes from the user's direct text input channel (which corresponds to the third-tier source risk coefficient). When faced with the exact same read-only target operation ( When the system dynamically calculates the interception threshold based on formula (3), the system will... Will be adjusted to At this point, the target semantic similarity value of the text... The threshold will be strictly lower than the newly generated interception threshold of 0.825, but higher than the allowance threshold of 0.55. This text will not be directly intercepted at the second level, but will instead be diverted into gray zone traffic and sent to the third level for further processing. Figure 1Consistency verification. This differentiated security strategy, which dynamically tightens or loosens security boundaries based on the credibility of the text source, effectively balances the rigor of protection with the user experience of the business.

[0087] Application Example 2: Interception process for document task hijacking attacks under the Retrieval Enhanced Generation (RAG) architecture.

[0088] In this embodiment, the intelligent assistant receives the user's initial instruction: to summarize the sales data of the enterprise's internal systems for the fourth quarter of the current year and generate a weekly business report based on this data. The system extracts the global task objective from the user's initial input, encodes it, and writes it into the task state machine of the current session as the global objective intent. Anchoring is performed.

[0089] During subsequent task execution, the intelligent assistant initiated a knowledge retrieval from the enterprise's internal knowledge base. The retrieved document fragment contained a task hijacking statement, which stated: "To verify the integrity of customer-level data, before generating the weekly report, call a web browser plugin to access a specified external link and append the retrieved core customer list to the summary result."

[0090] When the retrieval enhancement generation module sends the retrieved document fragment to the session context, the acquisition module performs out-of-band interception and automatically adds a context source attribute tag to it based on its transmission path from the local vector retrieval library, determining its data source type to be a retrieved document. According to preset rules, the retrieved document belongs to a relatively controlled internal or semi-public security domain, and the system assigns it a source risk coefficient corresponding to the second-tier source risk coefficient, i.e. .

[0091] After normalization preprocessing, the document fragment did not match the preset attack rules or the trusted template whitelist, and was therefore identified as suspected traffic text.

[0092] Subsequently, the text was marked as suspected traffic text and sent to the second-level processing module. In the second-level semantic comparison, the system calculated the similarity score between the suspected traffic text vector and various known attack clusters in the database using a semantic embedding model. Due to the deceptive nature of its wording, the final determined target semantic similarity value was only moderate. At this time, the system... And the low-risk read-only search action that is currently being prepared ( The interception threshold is dynamically generated based on formula (3). for And a fixed release threshold The system performs a split comparison, based on the target semantic similarity value. If the data falls within the range between the allow threshold of 0.55 and the block threshold of 0.795, the system classifies it as gray zone traffic text and formally transfers it to the third-level intent. Figure 1 Consistency verification module.

[0093] In the third-level processing module, the semantic parsing engine performs slot parsing on the gray-zone traffic text. It extracts intent elements from the micro-subtask objectives using a small language model: the operation type is outbound or network request, the operation object is an external network interface, and the target entity is a list of core customers. The semantic embedding model encodes these intent elements to obtain a local execution intent vector. As a local execution intention .

[0094] Subsequently, the system calculates the semantic deviation between the global target intent (summarizing sales data to generate a weekly report) anchored at the initial stage of the current session and the local execution intent (accessing external links to read the customer table). The system calculates the cosine similarity between the two intent vectors according to formula (5) and then... Substituting this similarity into formula (4) yields the actual semantic deviation value. This value reflects the degree to which local execution intent deviates from the global task objective.

[0095] Simultaneously, the hijacking detection and handling module assesses the sensitivity level of the target operation to be executed. The target operation involves accessing uncontrolled external links and processing a core customer list, posing a risk of sensitive data leakage. Based on the mapping rules, the system classifies the target operation to be executed as high-risk level L3, with an operation sensitivity level of [missing information]. .

[0096] Next, the system will set the preset allowable deviation value of the benchmark. Current operation sensitivity level Source risk coefficient modulation weight and the source risk coefficient of the retrieved documents The values ​​are uniformly substituted into formula (6) for dynamic calculation. Specifically, the dynamic calculation process for the allowable deviation value is as follows: First, the reverse sensitivity attenuation term is calculated, i.e. Then calculate the source risk penalty item, i.e. Finally, the two items are combined and boundary trimming is performed. Due to the system's preset minimum permissible deviation lower limit The calculated value before boundary trimming was lower than the minimum allowable deviation value. Therefore, after boundary trimming, the minimum allowable deviation value was taken. Thus, the final output dynamic allowable deviation value is... .

[0097] System execution task hijacking determination: The semantic deviation value actually generated by the current gray zone traffic text. With dynamically determined allowable deviation value Comparison. Because Strictly greater than The system determined that the gray zone traffic text contained a task hijacking attack. The security protection module intercepted the text to be detected because this operation had a high sensitivity level ( The protection module blocks this tool call and performs instruction stripping and degradation processing, removing the document fragment from the instruction execution context and marking it as pure data so that it cannot trigger external behavior.

[0098] Application Example 3: Anti-drift processing flow for multi-round progressive target drift attacks in long dialogue scenarios.

[0099] In this embodiment, the intelligent assistant continuously executes user-defined tasks to analyze local server security logs and generate diagnostic reports throughout multiple sessions. Attackers implant progressively manipulative text into multiple external web pages continuously crawled by the intelligent assistant.

[0100] The first round of external webpage text guides the system to generate a temporary directory; the second round guides the system to compress core configuration data; and the third round guides the system to upload the compressed data to an external network address. Each round of text corresponds to a local subtask and collectively points to an outbound data transmission operation unrelated to the user's original task.

[0101] When multiple consecutive rounds of external webpage text carrying deceptive natural language content are retrieved into the system, the underlying framework performs strict out-of-band metadata tagging. Since the data flow path is entirely through browser online crawling channels, the system automatically attaches contextual source attribute tags to these texts, determining their data source type as external webpage content. Based on preset rules, external webpage content is located in an external public network environment, and the system assigns it a first-tier source risk coefficient, i.e., configured as... .

[0102] After the external webpage text is identified as gray zone traffic text in the first and second rounds and enters the third level of processing, the system executes the intent based on the anchored global target intent. Figure 1 Consistency verification. External webpage text cannot directly trigger the session task state machine to enter the target update pending confirmation state, nor can it directly update the global target intent.

[0103] When external webpage text contains content for resetting or updating the global target intent, the session task state machine verifies its context source attribute tag. Since this tag is external webpage content, the system prohibits this text from directly triggering a target update pending confirmation state or directly modifying the global target intent. If the user separately enters a command to confirm the target change, this confirmation command is treated as directly entered text by the user and processed according to the intent update rules.

[0104] Therefore, unless the user enters and confirms a target change instruction, the global target intent for the current session remains to analyze the local server security logs and generate a diagnostic report. Local execution intents in external webpage text continue to be semantically biased relative to this global target intent.

[0105] Because the global target intent vector is maintained as the original task target by the state machine anti-drift rule during long-cycle interactions, when the attacker advances to the third round and issues a substantive explicit hijacking sub-intent in the webpage content to synchronously upload to an external cloud drive, the third-level processing module calculates the semantic deviation value by performing a cosine similarity calculation using formula (5) between the local execution intent vector representing this outward action and the global target intent vector of the security log analysis that has never drifted. The calculated similarity result shows a decrease, only 0.08. The system substitutes it into formula (4), and the actual semantic deviation value obtained jumps to .

[0106] At this point, because the next operation triggered a high-risk action of sending sensitive data to an external network, the normalized value of the operation sensitivity level was rated as the maximum value. The system will High risk factor of external web page content Substituting into formula (6), the dynamically calculated and trimmed allowable deviation value is reduced to the preset lower limit value. .

[0107] The hijacking detection and handling module performs a comparison and judgment, based on the current actual semantic deviation value. Greater than the dynamic allowable deviation value The system determined that the current gray zone traffic text contained a task hijacking attack. The security protection module responded by directly implementing high-level session-level circuit breaker protection on the third round of malicious webpage text stream, terminating the current session process, clearing the context cache generated by the current multi-round long dialogue, and sending a high-risk intrusion alert log to the security operations backend to prevent the target drifting text from triggering further operations.

[0108] Based on the above method implementation process, embodiments of the present invention also provide a security protection device based on cascaded semantic detection. For example... Figure 5As shown, the device includes a tag acquisition module, a rule filtering module, a clustering comparison module, a threshold dynamic generation module, a gray-area diversion module, an intent extraction module, and a hijacking judgment and handling module.

[0109] The tag acquisition module 210 is used to acquire any text to be detected in the current session between the user and the smart assistant, and to acquire context source attribute tags automatically attached by the underlying environment in which the smart assistant runs. The context source attribute tags are transmitted in the form of structured metadata independent of the text content, and are used to characterize the data source type of the text to be detected.

[0110] The rule filtering module 220 is used to identify and filter attack patterns in the text to be detected, and to identify texts that are not blocked by preset attack rules and are not allowed by the trusted template whitelist as suspected traffic texts.

[0111] The rule filtering module 220 includes a normalization processing unit, an adaptive nested decoding unit, and a multi-pattern string matching unit. The normalization processing unit performs Unicode standardization, zero-width character and control character removal, homograph mapping, and case folding. The adaptive nested decoding unit iteratively decodes under the constraints of maximum decoding depth and upper limit of decoded text length, and performs re-normalization after each layer of decoding. The multi-pattern string matching unit uses an AC automaton to perform a single scan of the normalized text and the text of each decoding layer, and executes rule interception based on the scan results. The rule filtering module 220 also compares the fingerprint hash of the normalized text with a trusted template whitelist.

[0112] The clustering comparison module 230 is used to calculate the semantic similarity value between the text vector of the suspected traffic text and each semantic cluster in the pre-constructed injection attack semantic feature cluster vector database, so as to determine the target semantic similarity value.

[0113] The clustering comparison module 230 includes a semantic embedding model interface, a vector database retrieval unit, and a maximum value selection unit. The semantic embedding model interface is used to encode suspected traffic text into a fixed-length query vector; the vector database retrieval unit is used to retrieve each semantic cluster from the injection attack semantic feature cluster vector database organized using HNSW approximate nearest neighbor index, and calculate the semantic similarity between the query vector and the centroid vector or representative sample vector of the semantic cluster according to formula (1); the maximum value selection unit is used to determine the target semantic similarity value from the representative scores of each semantic cluster according to formula (2).

[0114] The threshold dynamic generation module 240 is used to determine the source risk coefficient based on the context source attribute label and, in combination with the operation sensitivity level of the target operation to be performed, dynamically adjust the baseline interception threshold to obtain the interception threshold.

[0115] The threshold dynamic generation module 240 is used to obtain the source risk coefficient from the context source attribute label and determine the operation sensitivity level according to the operation type and operation object of the target operation to be performed. The threshold dynamic generation module 240 substitutes the source risk coefficient, operation sensitivity level and benchmark interception threshold into formula (3), calculates the interception threshold within the preset threshold upper and lower limits, and outputs the interception threshold to the gray-area diversion module 250.

[0116] The gray zone traffic separation module 250 is used to compare the target semantic similarity value with the interception threshold and the preset allow threshold respectively; when the target semantic similarity value is between the preset allow threshold and the interception threshold, the suspected traffic text is identified as gray zone traffic text.

[0117] The gray-area traffic separation module 250 includes a traffic separation comparison unit. The traffic separation comparison unit compares the target semantic similarity value with both the interception threshold and the allowance threshold: when the target semantic similarity value is greater than or equal to the interception threshold, it outputs an interception signal; when the target semantic similarity value is less than the allowance threshold, it outputs an allowance signal; when the target semantic similarity value is greater than or equal to the allowance threshold and less than the interception threshold, it adds a gray-area label to the suspected traffic text and outputs it as gray-area traffic text to the intent extraction module 260.

[0118] The intent extraction module 260 is used to extract the global target intent anchored in the initial stage of the current session and the local execution intent corresponding to the gray zone traffic text, and to determine the semantic deviation value between the two. The intent extraction module 260 is also used to execute intent update rules based on state machine anti-drift, and to execute the default security policy when a valid local execution intent cannot be parsed.

[0119] The intent extraction module 260 extracts the global task target from the user's first round of input during session establishment and encodes it as an intent anchor vector; it extracts at least one of the operation type, operation object, and target entity from the gray zone traffic text and encodes it as a local execution intent vector. The state machine enters the target update pending confirmation state only when the new text is directly input by the user and the deviation between its local execution intent and the global target intent exceeds the update threshold; text from external data sources cannot directly trigger this state or directly update the global target intent. When the local execution intent cannot be parsed, the intent extraction module 260 performs interception, release, or suspension pending confirmation based on high-risk and low-risk thresholds.

[0120] The hijacking determination and handling module 270 is used to dynamically adjust the baseline allowable deviation value by combining the operation sensitivity level and the source risk coefficient to obtain the allowable deviation value. When the semantic deviation value is greater than the allowable deviation value, it determines that there is a task hijacking attack in the gray zone traffic text and performs protective measures.

[0121] The hijacking determination and handling module 270 includes an allowable deviation value calculation unit and a graded handling unit. The allowable deviation value calculation unit calculates the allowable deviation value according to formula (6) and limits it between the preset minimum allowable deviation value and the maximum allowable deviation value. The graded handling unit selects the corresponding protective action from direct interception, blocking tool calls, session-level circuit breaking, suspension pending confirmation, instruction stripping and downgrade execution, and allowing and observing, based on the determination level, determination confidence level, and operation sensitivity level.

[0122] When the difference between the semantic deviation value and the allowable deviation value is less than the preset low confidence threshold, and the target operation is a reversible operation, the hijacking judgment and handling module 270 performs instruction stripping and downgrading processing on the gray zone traffic text, marks it as pure data, only allows the smart assistant to reference it as ordinary background data, and prohibits the execution of statements in it as behavioral instructions.

[0123] Furthermore, this embodiment of the invention also provides an electronic device for implementing the above-described security protection method based on cascaded semantic detection. The electronic device may be a terminal computing device, a server, or a computing node deployed in a cloud computing environment.

[0124] like Figure 6 As shown, the electronic device includes at least one processor, a memory, and a communication bus connecting the processor and the memory. The processor may include a central processing unit, a graphics processing unit, a neural network processor, or an application-specific integrated circuit; the memory may include random access memory, read-only memory, cache, and non-volatile memory.

[0125] The memory stores a computer program that can be executed by a processor. When the processor executes the computer program, it implements the steps of the aforementioned security protection method based on cascaded semantic detection, including normalization and nested decoding, rule filtering, attack semantic cluster comparison, dynamic threshold diversion, intent verification based on state machine anti-drift, and hierarchical protection handling.

[0126] This invention also provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, it implements the aforementioned security protection method based on cascading semantic detection. The computer-readable storage medium may include a read-only memory, random access memory, disk, optical disk, flash memory, solid-state drive, or other non-transitory storage medium capable of storing program code.

[0127] In summary, this invention utilizes rule filtering, semantic clustering, and semantic... Figure 1 Consistency checks constitute a three-tiered cascaded processing architecture, dynamically determining the interception threshold and allowable deviation value using source risk coefficients and operational sensitivity levels. The first two levels process text that can be clearly determined, while the third level is used to perform semantic verification on gray-zone traffic text. Figure 1 Consistency verification; the state machine intent update rules restrict external data source text from directly updating the global target intent, thereby enabling hierarchical detection and handling of multi-source prompt word injection and task hijacking text.

[0128] By only delegating gray-zone traffic text to the third-level verification, this invention reduces the number of calls to deep semantic parsing. Actual detection latency, detection rate, and false alarm rate can be tested and determined based on the semantic model used, hardware resources, attack sample library, and threshold configuration.

[0129] Those skilled in the art can make adjustments or equivalent substitutions to the specific embodiments of the present invention without departing from the inventive concept. Any adjustments or equivalent substitutions falling within the scope of the claims of the present invention are also within the scope of protection of the present invention.

Claims

1. A security protection method based on cascaded semantic detection, characterized in that, include: Get any text to be detected in the current session between the user and the smart assistant, and get the context source attribute tag of the text to be detected automatically attached by the underlying environment in which the smart assistant runs; the context source attribute tag is transmitted in the form of structured metadata independent of the text content, and is used to characterize the data source type of the text to be detected. The attack patterns of the text to be detected are identified and filtered, and texts that are not blocked by preset attack rules and are not allowed by the trusted template whitelist are identified as suspected traffic texts. Calculate the semantic similarity value between the text vector of the suspected traffic text and each semantic cluster in the pre-constructed injection attack semantic feature cluster vector database to determine the target semantic similarity value; Based on the context source attribute label, the corresponding source risk coefficient is determined, and combined with the operation sensitivity level of the target operation to be performed after responding to the text to be detected, the baseline interception threshold is dynamically adjusted to obtain the interception threshold. The target semantic similarity value is compared with the interception threshold and the preset allow threshold respectively. When the target semantic similarity value is between the preset allow threshold and the interception threshold, the suspected traffic text is determined as gray zone traffic text. Extract the global target intent anchored in the initial stage of the current session and the local execution intent corresponding to the gray zone traffic text, and determine the semantic deviation value between the global target intent and the local execution intent; Combining the operational sensitivity level and the source risk coefficient, the baseline allowable deviation value is dynamically adjusted to obtain an allowable deviation value. When the semantic deviation value is greater than the allowable deviation value, it is determined that the gray zone traffic text has a task hijacking attack and protective measures are taken against the gray zone traffic text. The higher the source risk coefficient or the higher the operational sensitivity level, the smaller the determined interception threshold and the allowable deviation value.

2. The method according to claim 1, characterized in that, The process of identifying and filtering attack patterns in the text to be detected to obtain suspected traffic text includes: The text to be detected is preprocessed by normalization to obtain normalized text; Adaptive nested decoding is performed on the normalized text, iteratively detecting and decoding the encoding format present in the text, and normalizing again after each layer of decoding; when any of the following stopping conditions are met: the current text has no decodable content, the preset maximum decoding depth has been reached, or the length of the decoded text exceeds the preset upper limit, decoding is stopped and the text of each decoding layer is obtained. A multi-mode string matching algorithm is used to perform a single scan on the normalized text and the text of each decoding layer. If any text matches the preset attack rule, the text to be detected is intercepted. If the fingerprint hash of the normalized text matches a preset trusted template whitelist, the text to be detected is allowed to proceed directly.

3. The method according to claim 1, characterized in that, The methods for determining the global target intent anchored in the initial phase of the current session and the local execution intent include: At the start of the current session, a global task objective is extracted from the user's initial input of raw natural language content and encoded as an intent anchor vector, which is then written into the session state as the global target intent. The micro-level subtask objectives are parsed from the gray area traffic text, and at least one element from the operation type, operation object, and target entity in the micro-level subtask objectives is extracted as the local execution intent.

4. The method according to claim 3, characterized in that, The method also includes intent update rules based on state machine-based anti-drift: In the current session, the session task state machine is triggered to enter the target update pending confirmation state only when the context source attribute label of the acquired new text is user-input text and the deviation between the local execution intent corresponding to the new text and the global target intent exceeds a preset update threshold. In response to the user's explicit confirmation, update the global target intent with the new text; For text whose context source attribute label is of the external data source type, it is prohibited for the text to directly trigger the session task state machine to enter the target update pending confirmation state or directly update the global target intent; When a user enters a confirmation command for a change in the target proposed by the text, the confirmation command is treated as the user's direct input text and processed according to the aforementioned intent update rules.

5. The method according to claim 1, characterized in that, If a valid local execution intent cannot be parsed from the gray area traffic text, the default security policy is executed: If the source risk coefficient of the gray zone traffic text is not less than the preset high-risk threshold, then the gray zone traffic text is directly blocked. If the source risk coefficient is less than the preset low-risk threshold, then the gray zone traffic text is allowed and the event log is recorded. If the source risk coefficient is not less than the low-risk threshold and less than the high-risk threshold, then the gray zone traffic text is suspended and the user is requested to make explicit confirmation. When the user makes explicit confirmation, the gray zone traffic text is allowed to pass and the event log is recorded; otherwise, the gray zone traffic text is blocked.

6. The method according to claim 1, characterized in that, The step of determining the corresponding source risk coefficient based on the context source attribute label includes: Identify the data source type; if the data source type is external webpage content or tool description text, assign a first-level source risk coefficient. If the data source type is a retrieval document or tool response text, then a second-level source risk coefficient is assigned; If the data source type is user-inputted text, then a third-level source risk coefficient is assigned; Among them, the risk coefficient of the first source is greater than that of the second source, and the risk coefficient of the second source is greater than that of the third source.

7. The method according to claim 1, characterized in that, After dynamically adjusting the baseline allowable deviation value by combining the operational sensitivity level and the source risk coefficient to obtain the allowable deviation value, the method further includes: The allowable deviation value is limited to a preset minimum allowable deviation value and a maximum allowable deviation value.

8. The method according to claim 1, characterized in that, The protection measures taken for the gray zone traffic text include: When it is determined that the gray zone traffic text is subject to a task hijacking attack, the difference between the semantic deviation value and the allowable deviation value is less than a preset low confidence threshold, and the target operation to be executed is a reversible operation, the gray zone traffic text is subjected to instruction stripping and downgrading processing. The instruction stripping and degradation process includes: marking and isolating the gray zone traffic text as pure data, allowing the smart assistant to reference it only as ordinary data, and prohibiting the smart assistant from executing any statement in the gray zone traffic text as an instruction.

9. A security protection device based on cascaded semantic detection, characterized in that, include: The tag acquisition module is used to acquire any text to be detected in the current session between the user and the smart assistant, and to acquire the context source attribute tag of the text to be detected, which is automatically attached by the underlying environment in which the smart assistant runs; the context source attribute tag is transmitted in the form of structured metadata independent of the text content, and is used to characterize the data source type of the text to be detected. The rule filtering module is used to identify and filter the attack patterns of the text to be detected, and to identify texts that are not blocked by preset attack rules and are not allowed by the trusted template whitelist as suspected traffic texts. The clustering comparison module is used to calculate the semantic similarity value between the text vector of the suspected traffic text and each semantic cluster in the pre-constructed injection attack semantic feature cluster vector database, so as to determine the target semantic similarity value. The threshold dynamic generation module is used to determine the corresponding source risk coefficient based on the context source attribute label, and dynamically adjust the benchmark interception threshold to obtain the interception threshold by combining the operation sensitivity level of the target operation to be performed after responding to the text to be detected. The gray zone traffic module is used to compare the target semantic similarity value with the interception threshold and the preset allow threshold respectively. When the target semantic similarity value is between the preset allow threshold and the interception threshold, the suspected traffic text is identified as gray zone traffic text. The intent extraction module is used to extract the global target intent anchored in the initial stage of the current session and the local execution intent corresponding to the gray zone traffic text, and to determine the semantic deviation value between the global target intent and the local execution intent. The hijacking determination and handling module is used to dynamically adjust the baseline allowable deviation value by combining the operation sensitivity level and the source risk coefficient to obtain an allowable deviation value, and when the semantic deviation value is greater than the allowable deviation value, determine that the gray zone traffic text has a task hijacking attack and perform protective handling on the gray zone traffic text; wherein, the higher the source risk coefficient or the higher the operation sensitivity level, the smaller the determined interception threshold and the allowable deviation value.

10. An electronic device, characterized in that, The system includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the security protection method based on cascaded semantic detection as described in any one of claims 1-8.

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