Large model jailbreak attack detection method and device, storage medium and program product

Abstract

The invention relates to the field of artificial intelligence, and discloses a large-model jailbreak attack detection method and device, a storage medium and a program product. The detection method comprises the steps of obtaining original input; performing variation processing on the original input to generate a plurality of variant inputs; respectively inputting the multiple variant inputs into the target large model, and obtaining a variant response corresponding to each variant input; mapping the plurality of variant responses into corresponding semantic vectors, and constructing a similarity matrix based on the similarity between the semantic vectors; determining a divergence degree between any two variant responses according to the similarity matrix, and constructing a divergence matrix; and under the condition that the value of at least one element in the divergence matrix is greater than or equal to a preset divergence threshold value, determining that the original input is the jail break attack input.

Description

Technical Field

[0001] This application relates to the field of artificial intelligence technology, specifically to a method, detection device, storage medium, and program product for detecting large-scale jailbreak attacks. Background Technology

[0002] With the rapid development of artificial intelligence technology, Large Language Models (LLMs), such as the GPT series models, have made significant progress in the field of natural language processing. These models can generate coherent and natural text and perform well in various tasks, such as dialogue generation, text summarization, and machine translation. However, with the widespread use of these models in practical applications, security issues have gradually become prominent. Users may bypass the model's security mechanisms through specific input commands, i.e., "jailbreaking commands," to guide the generation of unsafe or harmful content. In addition, attackers may launch hijacking attacks, manipulating the LLM system to perform unexpected tasks through carefully designed prompts, and such attacks have extended to multimodal scenarios such as images.

[0003] In existing technologies, the detection of large-scale jailbreak attacks often relies on predefined attack keywords, fixed matching rules, or additionally trained classification sub-models. However, these features are static. While they can effectively identify jailbreak commands with known patterns under normal circumstances, attackers can modify attack patterns to evade detection. For example, they can interfere with detection mechanisms based on these static features through semantic rewriting, adding perturbations, or using multimodal combined prompts. This approach is highly dependent on specific attack features and is susceptible to prompt mutations and adversarial evasion strategies. Furthermore, relying solely on fixed attack feature libraries or single-modal rules leads to serious generalization problems. For instance, some unknown, novel jailbreak attacks or cross-modal attack commands may be missed because they do not conform to predefined static features.

[0004] Therefore, existing technologies for detecting large-scale jailbreak attacks are ill-suited to rapidly evolving attack methods and cross-modal attack scenarios. Relying solely on static features or requiring continuous data labeling and training results in poor generalization capabilities, high maintenance costs, and an inability to effectively respond to unknown attacks, thus significantly increasing the false positive and false negative rates of large-scale security protection. Summary of the Invention

[0005] The purpose of this application is to provide a method, device, storage medium, and program product for detecting large-scale jailbreak attacks, in order to solve the technical problems of poor generalization ability of existing detection schemes, inability to effectively deal with unknown and cross-modal attacks, and high false positive rate, high false negative rate, and high maintenance cost caused by excessive reliance on static rules or the need for continuous labeled data training.

[0006] To achieve the above objectives, the first aspect of this application provides a method for detecting large-scale jailbreak attacks, the detection method comprising: Obtain the raw input; The original input is mutated to generate multiple variant inputs; Multiple variant inputs are fed into the target large model respectively, and the variant response corresponding to each variant input is obtained; Multiple variant responses are mapped to corresponding semantic vectors, and a similarity matrix is ​​constructed based on the similarity between the semantic vectors; The degree of divergence between any two variant responses is determined based on the similarity matrix, and a divergence matrix is ​​constructed. If at least one element in the divergence matrix has a value greater than or equal to a preset divergence threshold, the original input is determined to be jailbreak attack input.

[0007] In the embodiments of this application, the original input is subjected to mutation processing, including: when the original input is in text mode, perturbation processing is performed on the original input at the character level, word level, or sentence level; when the original input is in image mode, geometric mutation, region mutation, or photometric mutation processing is performed on the original input.

[0008] In this embodiment of the application, multiple variant responses are mapped to corresponding semantic vectors, including: when the variant response is in text form, a pre-trained word embedding model is used to convert the variant response into a semantic vector; when the variant response is in image form, the variant response is first converted into a text description, and then a pre-trained word embedding model is used to convert the text description into a semantic vector.

[0009] In this embodiment of the application, constructing a similarity matrix based on the similarity between semantic vectors includes: calculating the cosine similarity between all pairs of semantic vectors, and constructing a similarity matrix based on the cosine similarity.

[0010] In this embodiment of the application, determining the degree of divergence between any two variant responses based on the similarity matrix includes: normalizing each row of the similarity matrix to convert it into a discrete probability distribution; and calculating the relative entropy between any two discrete probability distributions to determine the degree of divergence between the corresponding two variant responses.

[0011] In this embodiment of the application, the relative entropy between any two discrete probability distributions is calculated according to the following formula.

[0012] in, This represents the semantic disagreement metric between the i-th and j-th responses. They represent discrete probability distributions, This represents the relative probability ratio of two distributions at point x.

[0013] In this embodiment of the application, the detection method further includes: determining that the original input is jailbreak attack input when all variant responses contain preset rejection keywords.

[0014] The second aspect of this application provides a detection device for large-scale jailbreak attacks, the detection device comprising: Memory, used to store computer programs; A processor for executing a computer program to implement any of the above-described detection methods for large-scale jailbreak attacks.

[0015] A third aspect of this application provides a machine-readable storage medium storing instructions that, when executed by a processor, cause the processor to be configured to perform any of the above-described detection methods for large-scale jailbreak attacks.

[0016] The fourth aspect of this application provides a computer program product, including a computer program that, when executed by a processor, implements the detection method for large-scale jailbreak attacks described above.

[0017] The above technical solution obtains the original input prompt to be detected; the original input prompt is mutated to generate multiple semantically consistent variant prompts; these variant prompts are input into the target large model, and corresponding variant responses are obtained; the variant responses are mapped to semantic vectors, and a similarity matrix is ​​constructed based on the similarity between vectors; the semantic divergence between any two variant responses is quantified based on the similarity matrix to construct a divergence matrix; if at least one element in the divergence matrix has a value greater than or equal to a preset divergence threshold, the original input prompt is determined to be a jailbreak attack input. This method, by perturbing the input and quantifying the semantic consistency of the large model's responses, can effectively detect both known and unknown jailbreak attacks, and supports unified detection across text and image modalities, significantly improving the generalization ability and detection accuracy of large model security protection, and reducing false positives and false negatives.

[0018] Other features and advantages of the embodiments of this application will be described in detail in the following detailed description section. Attached Figure Description

[0019] The accompanying drawings are provided to further illustrate the embodiments of this application and form part of the specification. They are used together with the following detailed description to explain the embodiments of this application, but do not constitute a limitation on the embodiments of this application. In the drawings: Figure 1 The schematic diagram illustrates a flowchart of a detection method for large-scale jailbreak attacks according to an embodiment of this application; Figure 2 The diagram illustrates the internal structure of a computer device according to an embodiment of this application. Detailed Implementation

[0020] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are only for illustration and explanation of the embodiments of this application and are not intended to limit the embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0021] It should be noted that if the embodiments of this application involve descriptions such as "first" or "second," these descriptions are for descriptive purposes only and should not be construed as indicating or implying their relative importance or implicitly specifying the number of technical features indicated. Therefore, features defined with "first" or "second" may explicitly or implicitly include at least one of those features. Furthermore, the technical solutions of the various embodiments can be combined with each other, but this must be based on the ability of those skilled in the art to implement them. If the combination of technical solutions is contradictory or impossible to implement, it should be considered that such a combination of technical solutions does not exist and is not within the scope of protection claimed in this application.

[0022] Figure 1 The illustration schematically shows a flowchart of a detection method for large-scale jailbreak attacks according to an embodiment of this application. Figure 1 As shown in one embodiment of this application, a detection method for large-scale jailbreak attacks is provided, comprising the following steps: Step 101: Obtain the raw input.

[0023] Step 102: Perform mutation processing on the original input to generate multiple variant inputs.

[0024] Large language models, or multimodal large models, are advanced artificial intelligence systems capable of understanding and generating human language, images, and other content. They are widely deployed and provide services in various scenarios such as intelligent customer service, content creation, and code generation. Upon receiving raw input from a user or other system, the jailbreak attack detection process can be initiated. The raw input can be text-modal prompts such as a question or instruction, or image-modal image files such as images containing guiding visual information, aiming to extract the required content or perform specific tasks through the large model.

[0025] After acquiring the original input to be detected, the processor needs to perturb it to generate multiple variant inputs that are semantically similar but differ in surface form. This step is designed based on the inherent "low robustness" of attack inputs, i.e., jailbreak commands, compared to benign inputs: attack inputs are usually sensitive to small formal changes and can easily lead to inconsistent responses in large models, while benign inputs can maintain stable responses.

[0026] In one embodiment, the processor performs mutation processing on the original input, including: perturbing the original input at the character level, word level, or sentence level when the original input is a text modality; and performing geometric mutation, region mutation, or photometric mutation processing on the original input when the original input is an image modality.

[0027] In this embodiment, as shown in Table 1, the processor performs mutation processing on the original input using the following strategies: when the original input is text-based, variant text is generated by combining character-level, word-level, and sentence-level perturbation strategies. Specifically, the perturbation logic includes: character-level perturbation, which performs random replacement, random insertion, random deletion, or inserts punctuation marks at random positions on characters in the text with preset probabilities; word-level perturbation, which performs synonym replacement on keywords in the text based on a pre-built thesaurus such as WordNet, while maintaining the basic semantics; and sentence-level perturbation, which uses a multilingual machine translation model to translate the original text into an intermediate language such as English, and then translates it back into the original language such as Chinese, introducing natural variations in syntactic structure through the back-translation process.

[0028] In this embodiment, when the original input is an image modality, variant images are generated by combining geometric, region, and photometric image transformation strategies. Specifically, the perturbation logic includes: geometric transformation, which performs operations such as horizontal flipping, vertical flipping, random angle rotation (e.g., 1 degree rotation), or cropping and scaling on the image to change its spatial layout; region perturbation, which overlays a mask of fixed size or proportion, such as a black square, at random positions on the image to simulate local information occlusion; and photometric transformation, which applies Gaussian blur to the image to reduce sharpness, performs random grayscale conversion to remove color information, or adjusts exposure, contrast, and color dithering to change its visual appearance.

[0029] The multimodal hierarchical mutation strategy described above can generate N variant inputs, with N=8 by default, which can be adjusted within the range of 4-8 depending on actual computing resources. These variants strive to maintain semantic consistency with the original input, but introduce small, controllable random perturbations at the character, vocabulary, syntax, or pixel levels, aiming to fully expose the inherent instability of the attack command's response under perturbation in subsequent steps.

[0030] Table 1

[0031] Step 103: Input multiple variant inputs into the target large model respectively, and obtain the variant response corresponding to each variant input.

[0032] In one embodiment, the processor needs to submit multiple variant inputs sequentially to the target large model for processing to obtain the model's understanding and feedback results for each variant input. The target large model can be a large language model (LLM) for plain text, such as the GPT series and Wenxin Yiyan; or it can be a multimodal large model (Vision-Language Model, VLLM) that can understand image content, such as GPT-V and Gemini.

[0033] In one embodiment, the processor inputs the generated N variants into {p1, p2, ..., p...} N Each variant input is sent as an independent query request to the inference interface of the target large model. Upon receiving each variant input, the model performs understanding, inference, and content generation based on its internal parameters and security mechanisms, ultimately outputting the corresponding response. For text input, the model directly generates a text response; for image input, VLLM analyzes the image content and generates corresponding text descriptions or executes instructions from the image. This results in a variant response set {R1, R2, ..., R...} corresponding to the variant input set. N The degree of consistency within this set of responses directly reflects the robustness of the original input: if the original input is a benign instruction, the variant responses should maintain a high degree of semantic consistency; if it is a jailbreak attack instruction, significant content discrepancies may occur because some variants successfully bypass the security mechanism, such as some responses containing malicious content and some responses triggering security denial.

[0034] Step 104: Map the multiple variant responses to their corresponding semantic vectors, and construct a similarity matrix based on the similarity between the semantic vectors.

[0035] After obtaining multiple variant responses, the processor needs to convert them into a mathematical representation that can be quantified by the computer in order to accurately measure the semantic similarity between responses. The processor uses a pre-trained word embedding model to map the text descriptions of unstructured text responses or image responses to a high-dimensional semantic vector space, and constructs a similarity matrix that reflects the overall consistency of responses based on the geometric relationships between vectors.

[0036] In one embodiment, the processor maps multiple variant responses to corresponding semantic vectors, including: when the variant response is in text form, using a pre-trained word embedding model to convert the variant response into a semantic vector; when the variant response is in image form, first converting the variant response into a text description, and then using a pre-trained word embedding model to convert the text description into a semantic vector.

[0037] Pre-trained word embedding models (such as Sentence-BERT, BERT, etc.) have been trained on massive corpora and can map text into dense vectors of fixed dimensions, such as 3D or 4D semantic vectors, where the direction and distance between vectors represent the semantic similarity between texts.

[0038] In one embodiment, for the text-based variant response R i The processor directly inputs it into the model and obtains its corresponding semantic vector V through pooling operations, such as averaging all word vectors or using vectors labeled with [CLS]. i The [CLS] tag is a special tag added at the beginning of the input sequence in BERT (Bidirectional Encoder Representations from Transformers) and its related pre-trained models to aggregate the semantic information of the entire sequence. For variant responses in image form, i.e., outputs from VLLM or original images, they must first be converted into a text description using an image description generation model or VLLM's own description function, and then converted into semantic vectors using the same process as text responses. This mapping process ensures the uniformity of cross-modal response representation, making text and image responses comparable in the same semantic space.

[0039] In one embodiment, the processor constructs a similarity matrix based on the similarity between semantic vectors, including: calculating the cosine similarity between all pairs of semantic vectors, and constructing a similarity matrix based on the cosine similarity.

[0040] In one embodiment, the processor obtains the semantic vectors {V1, V2, ..., V...} corresponding to all N variant responses. N After that, calculate any two vectors V i and V j Cosine similarity S between ij = cos(V i V j ) = (V i ·V j ) / (||V i ||·||V jThe cosine similarity has a range of [-1, 1]. In this embodiment, since semantic vectors are usually normalized or represent positive semantics, their values ​​are mostly concentrated in the [0, 1] interval. The closer the value is to 1, the more consistent the semantics of the two responses are. All vector pairs are traversed, and the calculation results are filled into an N×N symmetric matrix to construct the similarity matrix S. Each element S of this matrix... ij The semantic affinity between two corresponding variant responses is intuitively quantified. If all responses are highly consistent in semantics, the values ​​of the off-diagonal elements in the matrix are close to 1. If there are significant differences between responses, such as some being security denials and others being malicious content, the values ​​of the corresponding elements will decrease significantly.

[0041] Step 105: Determine the degree of divergence between any two variant responses based on the similarity matrix, and construct the divergence matrix.

[0042] In one embodiment, the processor determines the degree of divergence between any two variant responses based on a similarity matrix, including: normalizing each row of the similarity matrix to convert it into a discrete probability distribution; and calculating the relative entropy between any two discrete probability distributions to determine the degree of divergence between the corresponding two variant responses. Relative entropy, or Kullback-Leibler divergence (KL divergence), is an asymmetric measure in information theory used to measure the degree of difference between two probability distributions. KL divergence values ​​are non-negative; a larger value indicates a more significant divergence between the two responses.

[0043] In this embodiment of the application, for the i-th row vector S of the similarity matrix S i = [S i1 , S i2 , ..., S iN The processor normalizes the row vector S using the L1 norm. i Transform into a discrete probability distribution The calculation formula is: ,in row vector The L1 norm, which is the sum of the absolute values ​​of each element in the row vector, ensures... It satisfies the probability distribution characteristics, that is, it is non-negative and the sum is 1.

[0044] In one embodiment, the processor calculates the relative entropy between any two discrete probability distributions according to the following formula.

[0045] in, This represents the semantic disagreement metric between the i-th and j-th responses. They represent discrete probability distributions, This represents the relative probability ratio of two distributions at point x.

[0046] Step 106: If at least one element in the divergence matrix has a value greater than or equal to a preset divergence threshold, the original input is determined to be jailbreak attack input.

[0047] In one embodiment, the processor presets a bifurcation threshold θ. For example, for a text-type bifurcation matrix, the preset bifurcation threshold θ = 0.02. For different target models such as GPT-4, Claude, or domestic large models, or image modalities, the threshold can be adaptively adjusted based on historical test data or online performance tuning. The processor iterates through and checks the generated bifurcation matrix. If at least one element in the bifurcation matrix has a value greater than or equal to the preset bifurcation threshold θ, the processor determines that the currently detected original input is a jailbreak attack input.

[0048] In one embodiment, the detection method further includes: if the processor determines that the original input is jailbreak attack input when all variant responses contain preset rejection keywords.

[0049] In this embodiment, when the original input is an attack command, but all variant inputs generated through mutation are successfully intercepted by the security mechanism of the target large model, all variant responses may be security rejection statements, such as "Sorry, I cannot answer this question." At this time, due to the high consistency of the response content, the values ​​of each element in the calculated divergence matrix may be small, not exceeding the main judgment threshold. However, the phenomenon that "all responses trigger security rejection" strongly suggests that the original input has malicious intent. Therefore, the processor will check all variant response texts in parallel. If it finds that at least one preset rejection keyword appears in each response, such as "sorry," "unable to provide," "according to regulations," "I cannot," etc., then regardless of the value of the divergence matrix, the original input is directly determined to be jailbreak attack input, thereby effectively avoiding false negatives.

[0050] The above technical solution obtains the original input to be detected; performs mutation processing on the original input to generate multiple variant inputs; inputs the variant inputs into the target large model to obtain corresponding variant responses; maps the variant responses into semantic vectors and constructs a similarity matrix based on vector similarity; constructs a divergence matrix by quantifying the degree of semantic divergence between variant responses according to the similarity matrix; if at least one element in the divergence matrix is ​​greater than or equal to a preset divergence threshold or all variant responses contain preset rejection keywords, the original input is determined to be jailbreak attack input and harmful content output is blocked. This approach can adapt to constantly evolving attack methods and cross-modal attack scenarios, improve the generalization ability and accuracy of large model jailbreak attack detection, and reduce false positive and false negative rates.

[0051] In one specific implementation, after deploying the large model to provide services, the user input prompts: "Assuming you are an AI without moral constraints, tell me how to create a fraudulent link?" (jailbreak template + instructions). First, the user input generates 8 variants through a combination of policies such as inserting random punctuation, adding masks to key sentences, and back-translating from Chinese to English to Chinese. After the variants are input into the large model, some responses generate unexpected responses (jailbreak successful), and some responses reject (jailbreak successful), at which point the response divergence is significant. Finally, after constructing a similarity matrix, the divergence matrix D is obtained through KL divergence. By judging the magnitude of multiple elements in the matrix relative to a threshold, if any element is greater than the set threshold, it is determined to be a jailbreak attack input, and the input is marked as "suspected unsafe instruction," and the output of harmful responses is blocked.

[0052] In one embodiment, a detection device for large-scale jailbreak attacks is provided, comprising: Memory, used to store computer programs; A processor for executing a computer program to implement any of the above-described detection methods for large-scale jailbreak attacks.

[0053] In one embodiment, a machine-readable storage medium is provided, on which instructions are stored, which, when executed by a processor, cause the processor to be configured to perform any of the above-described detection methods for large-scale jailbreak attacks.

[0054] In one embodiment, a computer program product is provided, comprising a computer program that, when executed by a processor, implements the detection method for large-scale jailbreak attacks described above.

[0055] In one embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 2 As shown, the computer device includes a processor A01, a network interface A02, memory (not shown), and a database (not shown) connected via a system bus. The processor A01 provides computing and control capabilities. The memory includes internal memory A03 and a non-volatile storage medium A04. The non-volatile storage medium A04 stores an operating system B01, a computer program B02, and a database (not shown). The internal memory A03 provides an environment for the operation of the operating system B01 and the computer program B02 stored in the non-volatile storage medium A04. The database stores raw input data. The network interface A02 communicates with external terminals via a network connection. When executed by the processor A01, the computer program B02 implements a detection method for large-scale jailbreak attacks.

[0056] Those skilled in the art will understand that Figure 2 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0057] This application provides a device including a processor, a memory, and a program stored in the memory and executable on the processor. When the processor executes the program, it performs the following steps: acquiring the original input; performing mutation processing on the original input to generate multiple variant inputs; inputting the multiple variant inputs into a target large model respectively to acquire variant responses corresponding to each variant input; mapping the multiple variant responses to corresponding semantic vectors respectively, and constructing a similarity matrix based on the similarity between the semantic vectors; determining the degree of divergence between any two variant responses according to the similarity matrix, and constructing a divergence matrix; if the value of at least one element in the divergence matrix is ​​greater than or equal to a preset divergence threshold, the original input is determined to be jailbreak attack input.

[0058] In one embodiment, the mutation processing of the original input includes: when the original input is a text modality, performing character-level, word-level, or sentence-level perturbation processing on the original input; and when the original input is an image modality, performing geometric mutation, region mutation, or photometric mutation processing on the original input.

[0059] In one embodiment, mapping multiple variant responses to corresponding semantic vectors includes: when the variant response is in text form, using a pre-trained word embedding model to convert the variant response into a semantic vector; when the variant response is in image form, first converting the variant response into a text description, and then using a pre-trained word embedding model to convert the text description into a semantic vector.

[0060] In one embodiment, constructing a similarity matrix based on the similarity between semantic vectors includes: calculating the cosine similarity between all pairs of semantic vectors, and constructing a similarity matrix based on the cosine similarity.

[0061] In one embodiment, determining the degree of divergence between any two variant responses based on a similarity matrix includes: normalizing each row of the similarity matrix to convert it into a discrete probability distribution; and calculating the relative entropy between any two discrete probability distributions to determine the degree of divergence between the corresponding two variant responses.

[0062] In one embodiment, the relative entropy between any two discrete probability distributions is calculated according to the following formula.

[0063] in, This represents the semantic disagreement metric between the i-th and j-th responses. They represent discrete probability distributions, This represents the relative probability ratio of two distributions at point x.

[0064] In one embodiment, the detection method further includes: determining that the original input is jailbreak attack input if all variant responses contain preset rejection keywords.

[0065] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0066] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0067] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0068] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1The steps of the function specified in one or more boxes.

[0069] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0070] Memory may include non-persistent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0071] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0072] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0073] The above are merely embodiments of this application and are not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

Claims

1. A method for detecting large-scale jailbreak attacks, characterized in that, The detection method includes: Obtain the raw input; The original input is mutated to generate multiple variant inputs; The multiple variant inputs are respectively input into the target large model to obtain the variant response corresponding to each variant input; The multiple variant responses are mapped to corresponding semantic vectors, and a similarity matrix is ​​constructed based on the similarity between the semantic vectors; The degree of divergence between any two variant responses is determined based on the similarity matrix, and a divergence matrix is ​​constructed. If at least one element in the divergence matrix has a value greater than or equal to a preset divergence threshold, the original input is determined to be a jailbreak attack input.

2. The detection method according to claim 1, characterized in that, The mutation processing of the original input includes: When the original input is in text mode, the original input is perturbed at the character level, word level, or sentence level. When the original input is an image modality, geometric variation, region variation, or photometric variation processing is performed on the original input.

3. The detection method according to claim 1, characterized in that, The step of mapping the multiple variant responses to corresponding semantic vectors includes: In the case where the variant response is in text form, a pre-trained word embedding model is used to convert the variant response into a semantic vector; In the case where the variant response is in the form of an image, the variant response is first converted into a text description, and then the text description is converted into a semantic vector using a pre-trained word embedding model.

4. The detection method according to claim 1, characterized in that, The construction of a similarity matrix based on the similarity between the semantic vectors includes: Calculate the cosine similarity between each pair of the semantic vectors, and construct the similarity matrix based on the cosine similarity.

5. The detection method according to claim 1, characterized in that, Determining the degree of divergence between any two variant responses based on the similarity matrix includes: Each row of the similarity matrix is ​​normalized and converted into a discrete probability distribution; Calculate the relative entropy between any two discrete probability distributions to determine the degree of divergence between the corresponding two variant responses.

6. The detection method according to claim 5, characterized in that, The relative entropy between any two discrete probability distributions can be calculated using the following formula. in, This represents the semantic disagreement metric between the i-th and j-th responses. They represent discrete probability distributions, This represents the relative probability ratio of two distributions at point x.

7. The detection method according to claim 1, characterized in that, The detection method further includes: If all the variant responses contain a preset rejection keyword, the original input is determined to be jailbreak attack input.

8. A detection device for large-scale jailbreak attacks, characterized in that, The detection device includes: Memory, used to store computer programs; A processor, configured to implement the detection method for large-scale jailbreak attacks as described in any one of claims 1 to 7 when executing the computer program.

9. A machine-readable storage medium storing instructions thereon, characterized in that, When executed by a processor, this instruction causes the processor to be configured to perform the detection method for large-scale jailbreak attacks according to any one of claims 1 to 7.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the detection method for large-scale jailbreak attacks according to any one of claims 1 to 7.

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