881 results about "Vulnerability detection" patented technology

The invention discloses a deep learning artificial intelligence security vulnerability detection platform, and relates to the technical field of intelligent detection, and the platform comprises an information processing module which collects heterogeneous data in real time, carries out the labeling, format unification and modal aggregation processing of the data, and generates a sample set; the feature learning module is used for performing feature unwrapping on the sample set by using a variational auto-encoder, extracting modal data features and potential space representation learning, and outputting a potential space vector; the response generation module is used for generating a vulnerability response strategy through a modal consistency verification and response template matching mechanism based on the vulnerability risk level vector in combination with a response generation engine; and the repair feedback module is used for executing automatic vulnerability repair operation in combination with federal reinforcement learning and Bayesian optimization, performing feedback optimization according to an execution result, and outputting the vulnerability repair operation and a feedback result. According to the method, the response strategy is combined with intelligent matching of the real-time risk level, so that the accuracy and adaptability of vulnerability repair are improved.

The invention discloses an intelligent contract vulnerability detection and repair system based on a heterogeneous graph neural network, and belongs to the technical field of block chain security, and the system comprises a contract analysis module, a multilayer graph construction module, a heterogeneous graph neural network module, a vulnerability feature library, a vulnerability recognition engine, an automatic repair module and a visual interface. After the source code of the intelligent contract is input, code analysis and standardization are completed by a contract analysis module; the multi-layer graph construction module constructs a contract internal heterogeneous graph, an inter-contract interaction graph and an ecosystem relation graph based on a graph theory; the heterogeneous graph neural network module learns a vulnerability feature mode; the vulnerability recognition engine combines the vulnerability feature library to realize vulnerability classification and risk assessment; the automatic repairing module generates a repairing scheme; and the visual interface realizes detection progress monitoring, result display and encrypted report export. The intelligent contract vulnerability detection and restoration system based on the heterogeneous graph neural network provided by the invention provides technical support for block chain digital asset security and ecological stability.

The invention provides an Internet of Vehicles vulnerability management method, system, device and medium based on a block chain, and relates to the technical field of vehicle networks, the method can comprehensively identify potential safety risks of vehicles through a dynamic and static combined vulnerability detection mechanism, and generates a structured vulnerability report; hash abstract chaining evidence storage is performed on key data of reports and subsequent repair links by using a block chain, so that the whole process of vulnerability discovery, analysis and repair is ensured not to be tampered and traceable, and the authenticity and credibility of data are improved; the integrity of the report is verified at the cloud end, and an AI analysis engine is combined to associate a CVE database and threat intelligence, so that intelligent generation and decision support of a repair scheme are realized; the hash abstract on the chain of the OTA patch is verified at the vehicle end, so that the credibility of the patch source and the integrity of the content are guaranteed; and finally, through feedback of an installation result and secondary uplink archiving, complete closed-loop management from vulnerability discovery to repair verification is formed.

The invention discloses a vulnerability hidden danger intelligent detection method based on a large model, and the method comprises the steps: firstly carrying out the global static analysis of a source code set, constructing a complete call graph and a complete data flow graph of a program, and forming a structured code knowledge graph; and then, aiming at the identified candidate vulnerability slices, based on the maps, carrying out accurate context retrieval and enhancement, converting key information such as a call chain and a data traceability path which are strongly related to the vulnerability slices into natural language description which can be understood by a large language model, and injecting the natural language description into cue words, so that missing global context information is provided for the model. And the defect of complex code analysis capability is overcome. In this way, the problem that an attention mechanism loses efficacy in remote code association is solved, and the accuracy and reliability of vulnerability detection are remarkably improved.

The invention discloses a cross-chain smart contract vulnerability detection method and system based on multi-feature fusion learning. The method comprises the following steps: collecting a cross-chain smart contract vulnerability data set for cleaning and labeling; feature extraction is carried out from the source code and the byte code, an abstract syntax tree (AST) is extracted from the cleaned source code, a basic control flow graph (CFG) is extracted from the byte code, and a cross-chain control flow graph (xCFG) is constructed; carrying out feature representation on AST and xCFG, generating a graph vector through a graph neural network (GNN), generating a semantic vector through CodeBert, and fusing the semantic vector into a feature fusion vector; performing model training and detection, taking the generated vectors as training data and test data, obtaining a cross-chain smart contract vulnerability detection model by adopting Transform-FC model training data, and finally evaluating model performance through accuracy, recall rate, precision rate and F1 value. According to the method, the structural features and semantic features of the codes can be effectively fused, potential vulnerability information in the codes can be fully mined, the recognition capability of the model for cross-chain vulnerabilities can be enhanced, and the accuracy and reliability of the cross-chain vulnerability detection model can be improved, so that the security of a block chain system can be more efficiently guaranteed.

The invention discloses an intelligent contract vulnerability detection method based on a heterogeneous graph attention network. According to the method, firstly, the source code of the intelligent contract is preprocessed, the SCIR of the code of the intelligent contract is constructed, the complexity of the code of the contract is reduced, and vulnerability features are enriched; and constructing an intelligent contract code attribute graph SCPG based on SCIR, integrating various code graph structures such as an abstract syntax tree and a control flow graph, and comprehensively describing syntax and semantic features of the contract. And then constructing an intelligent contract code heterogeneous graph SCHG on the basis of the SCPG, optimizing code graph structure representation, and realizing high-quality modeling of node features. And finally, detecting the vulnerability of the smart contract by using a customized multi-layer heterogeneous graph attention network model MHGAN. The intelligent contract vulnerability detection method based on deep learning makes up for the defects of an existing intelligent contract vulnerability detection method based on deep learning, effectively improves the accuracy of intelligent contract vulnerability detection, and is excellent in the interpretability of the detection result.

An industrial security system leverages generative artificial intelligence (AI) to automate the process of identifying software or hardware insecurities on industrial assets, generate recommendations for mitigating these vulnerabilities, and, where appropriate, deploy countermeasures to the vulnerable assets. By leveraging automated asset discovery, real-time asset and network monitoring, and generative AI-assisted vulnerability detection and remediation, the system can reduce the amount of time spent by security administrators in identifying and closing security vulnerabilities within their plant environments, and can alert administrators of potential security issues before those issues become critical.

The invention relates to the technical field of information processing, in particular to a supply chain cross-packet vulnerability detection method, device and equipment and a storage medium. A vulnerability packet name, a sensitive API, a trigger parameter and vulnerability description are integrated into tetrad information; if so, performing cross-packet call chain analysis on the source code file by using a cross-packet chain reachability analysis algorithm, obtaining a function call sequence of the sensitive API based on a cross-packet call chain analysis result, realizing vulnerability detection on a cross-packet call chain, generating a vulnerability verification code based on tetrad information by using a preset large language model, and performing vulnerability verification on the vulnerability verification code. The method comprises the following steps: establishing a function call sequence of a bug verification code, verifying the accessibility of the bug verification code in the function call sequence, determining the bug confidence according to the energy consumption condition of a large language model, and generating bug alarm information when the accessibility verification result is that the bug is accessible and the bug confidence is high, thereby realizing double judgment of the bug, reducing the false alarm rate of the bug and improving the user satisfaction.

The invention discloses a code vulnerability detection method, a code vulnerability rule configuration method, a static single assignment SSA code acquisition method, devices corresponding to the methods, and electronic equipment. The code vulnerability detection method comprises the following steps: acquiring a plurality of source code files and code vulnerability rules of an application program; generating an abstract syntax tree of the source code file, and obtaining a function call relationship; and traversing the abstract syntax tree, and obtaining a cross-function risk propagation path according to the function call relationship and the code vulnerability rule. By the adoption of the processing mode, the function calling relation and the abstract syntax tree are fused to conduct cross-function code vulnerability detection, cross-function vulnerability detection of context sensitivity, flow sensitivity and domain sensitivity is achieved, and vulnerability false report and missing report are avoided; therefore, the accuracy and recall rate of vulnerability detection can be effectively improved.

The invention relates to an automatic code auditing method and device, computer equipment and a storage medium. The automatic code auditing method comprises the steps of obtaining a grammar structure, a control flow and a data flow of a to-be-audited code; constructing a context graph of the to-be-audited code according to the grammatical structure, the control flow and the data flow of the to-be-audited code; obtaining a multi-modal collaborative vulnerability detection method, wherein the multi-modal collaborative vulnerability detection method comprises a static analysis method based on rule matching, a symbolic execution method based on a code path, a large model reasoning method based on semantic understanding and weights of the methods; and identifying one or more code vulnerabilities, the vulnerability type of each code vulnerability and the confidence coefficient according to the context graph of the to-be-audited code and the multi-modal collaborative vulnerability detection method. According to the method, the audit codes of various vulnerability types can be processed while the code audit efficiency can be improved.

The invention discloses an unauthorized vulnerability detection method, device and equipment and a readable storage medium, and is applied to the field of security detection, and the method comprises the steps: carrying out the semantic recognition of real business flow data through a large language model, and determining a to-be-detected interface; performing semantic analysis on the parameters of the to-be-detected interface by using a large language model to determine target parameters; extracting a parameter value with an unauthorized vulnerability risk in the target parameter from the historical real service flow data; generating a test effective load of the to-be-detected interface based on the target parameter and the parameter value by utilizing a large language model and a preset rule base; and performing unauthorized vulnerability detection on the to-be-detected interface by using the test payload, and determining a detection result. According to the method, the natural language understanding capability of a large language model is utilized, the limitation of traditional regularization preprocessing and effective load generation is broken through, and the method is adaptive to diversified scenes of a complex system.

The invention relates to the technical field of block chains, in particular to a smart contract vulnerability detection method and device based on multi-modal features, and the method mainly comprises the steps: training a meta-learning model in a dynamic adaptation module, and adjusting the global parameters of a modal feature extraction module, a dynamic gating fusion module and a classifier through the meta-learning model, the dynamic adaptation module comprises a meta-learning model constructed based on an MAML framework, and is used for optimizing global parameters of each module according to vulnerability features learned in pre-training; and inputting the multi-modal fusion feature vector into a classifier, and generating and outputting a vulnerability detection result of the smart contract. According to the method, known vulnerabilities can be accurately detected by fusing multi-modal features, and novel vulnerabilities can be rapidly adapted and detected.

The invention relates to a computer code vulnerability detection method and system based on artificial intelligence, and the method comprises the steps: obtaining a detection demand input by a user, analyzing the detection demand based on a natural language processing algorithm, and extracting a target source code set corresponding to the detection demand from a to-be-detected code warehouse; performing standardization processing on the target source code set, and constructing a code feature data set; inputting the code feature data set into the trained vulnerability detection model, identifying to obtain a vulnerability code snippet, and generating a vulnerability score and a vulnerability type; and grading the vulnerability code snippets according to the vulnerability scores, generating repair suggestions in combination with vulnerability types, and outputting a corresponding detection report which comprises risk levels and the repair suggestions. The method has the effect of improving the accuracy of vulnerability detection.

The invention relates to a notebook software vulnerability scanning method based on a security policy. According to the method, firstly, a security policy library of a target notebook is obtained, and the security policy library comprises access control rules, data operation limiting conditions and vulnerability detection reference requirements for different software types; collecting software running data of the target notebook computer, wherein the software running data comprises current running process information, file system operation records, network connection states and software configuration parameters; performing matching analysis on the software operation data and the security policy library to generate a preliminary scanning result; evaluating the risk level of the software vulnerability according to the preliminary scanning result; and finally, based on the risk level and the repair guide terms in the security policy library, generating software vulnerability repair guide information. Therefore, notebook software vulnerabilities can be scanned comprehensively and accurately, and effective repair guidance is provided.

The invention belongs to the technical field of software security, and particularly discloses a warehouse-level vulnerability detection method and device based on a large model function semantic abstract. The method comprises the following steps: generating a vulnerability detection abstract syntax tree according to a to-be-detected source code; generating an assembly prompt word according to the complete source code of the node function in the unprocessed state and the target semantic abstract, and analyzing the node function in the unprocessed state according to the assembly prompt word by calling the target large model; and determining a vulnerability detection result of the to-be-detected source code according to the initial function semantic abstract and the structured function semantic abstract. Through the above mode, the vulnerability detection abstract syntax tree is generated by using the program structure modeling strategy and the full function call graph is constructed, and then the semantic abstract of the structured function is determined by using the semantic understanding capability of the target large model, that is, deep security analysis and vulnerability mining are performed from the local function to the global context. Therefore, the accuracy and comprehensiveness of warehouse-level vulnerability detection can be effectively improved.

Systems, computer program products, and methods are described herein for vulnerability detection and mitigation using artificial intelligence (AI) in an electronic environment. The present disclosure is directed to a system that receives data from at least one data source and generates at least one vulnerability attribute of a network device based on at least the data. In addition, the system applies the vulnerability attribute to an AI engine. Using the AI engine, the system assigns at least one weight to the vulnerability attribute of the network device. After the at least one weight is assigned, the system generates, using the AI engine, a vulnerability matrix of the network device based on the at least one weight.

The invention discloses a network application vulnerability automatic detection method and system based on a large language model, and solves the problems of low vulnerability detection efficiency and low accuracy in the prior art. The method comprises the following steps of: S1, searching a path of an application programming interface (API) (Application Program Interface); s2, a big language model vulnerability analysis step; s3, a step of generating a script of a vulnerability PoC (Proof Of Concept: Conceptual Verification); s4, an IDOR (Insecure Direct Object Reference) vulnerability crawler identification step is carried out, and the step S4 is carried out according to the vulnerability crawler identification step and the step S4, the step S4 is carried out according to the vulnerability crawler identification step, and the step S4 is carried out according to the vulnerability crawler identification step. S5, a vulnerability verification step; and S6, a report generation step. According to the method and the system, the whole process from path searching to vulnerability hypothesis generation to automatic vulnerability verification is realized through driving the synergistic effect of multiple modules, the targets of zero false alarm and near-zero manual confirmation are achieved, the efficiency and the accuracy of network application security testing are greatly improved, and powerful support is provided for network security protection.

The invention provides a context enhanced vulnerability detection method based on a large language model, and belongs to the technical field of code security detection. Comprising the following steps: S1, data preprocessing: determining a code analysis range and preprocessing code data; s2, abstract extraction of the primitive API: executing cross-function deep analysis on the basis of code representation obtained by data preprocessing, and extracting an abstract used by the primitive API as context enhancement information of vulnerability detection. And S3, large language model vulnerability detection: inputting the source code of the target function and the additional primitive API abstract into the large language model as prompts, and carrying out automatic vulnerability detection and judgment. According to the method, the accuracy of cross-function vulnerability detection is greatly improved, analysis noise and resource consumption are effectively reduced, and the code vulnerability reasoning capability of a large language model is remarkably enhanced.

The invention belongs to the technical field of vulnerability detection, and particularly relates to a source code vulnerability detection method and system based on semantic comparative learning. Comprising the steps of obtaining source codes, preprocessing the source codes, and generating positive and negative samples paired with the source codes; improving the CodeBERT model to obtain an encoder, and building a momentum contrast learning model comprising a vulnerability classification branch, a representation consistency branch and a contrast learning branch based on the obtained encoder; training the momentum contrast learning model comprising the three branches by adopting a contrast learning method; and based on the trained momentum contrast learning model, performing vulnerability detection on a to-be-analyzed code. According to the method, a semantic contrast learning-driven source code vulnerability detection framework is designed and realized, and the problems of limitation of traditional static analysis and insufficient generalization ability of an existing deep learning model are solved by learning representation which is robust to code semantic changes and sensitive to vulnerability modes.

The invention discloses a large visual language model vulnerability detection method and device based on security sensitive layer activation guidance and a storage medium, and belongs to the technical field of artificial intelligence security, the method comprises the following steps: S1, constructing a security sensitive layer activation induction sample data set; s2, performing quantitative analysis on activation differences of the model intermediate layer under attack and normal input; s3, on the basis of the recognized security sensitive layer combination and an output layer of the large visual language model, generating an in-process confrontation image with an attack attribute through optimization of a loss function; and S4, recording an optimal security sensitive layer combination and a harmful response path guided by the optimal security sensitive layer combination, and constructing a structured representation of the potential security vulnerabilities of the model. According to the method, the security fragile links of the model are accurately positioned, the attack guiding force is improved in combination with multiple losses, the generated confrontation disturbance is controllable and imperceptible, the good cross-instruction migration capability is achieved, and the method adapts to various security assessment and red team test tasks.

The invention relates to the technical field of network security, and discloses a multi-agent cooperative RESTful API vulnerability test method. Constructing a parameter structured representation by analyzing an API interface specification, generating a test value conforming to service semantics by using a large language model, and establishing a semantic request baseline representing normal service logic; constructing a multi-agent collaborative architecture including vulnerability reasoning, case generation, test execution, test analysis and test management; all the agents are coordinated to carry out iterative vulnerability testing through a signal mechanism and a state transition table, and API service analysis, test strategy customization, use case directional variation and execution, result study and judgment and strategy optimization are achieved; and finally generating a structured vulnerability report. According to the method, the problems of semantic blind areas, shallow analysis layers, insufficient adaptive capacity and the like are solved, security risks such as business logic vulnerabilities and sensitive data leakage in the RESTful API can be deeply detected, and the accuracy and coverage rate of vulnerability detection are remarkably improved.

The invention discloses an API-oriented dynamic fuzz testing and vulnerability detection method, and belongs to the technical field of software security testing. The method comprises the following steps: extracting a dependency relationship, an input parameter, an output response and context state data of API calling, generating an initial API dependency graph, dynamically updating by capturing API state change in real time, forming an API state graph, and executing boundary-oriented variation based on parameter constraint characteristics, so as to obtain an API state graph; generating a variation test parameter, calling an API (Application Program Interface) of the variation test parameter to monitor a process memory behavior and response metadata, and generating a multi-dimensional abnormal signal; and performing mode matching on the abnormal signal and the vulnerability feature knowledge base, outputting a vulnerability type label and generating a path tracing report. According to the method, a context state sensing dynamic graph modeling technology is adopted, and a boundary-oriented intelligent variation strategy and multi-source abnormal behavior collaborative analysis are combined, so that precise vulnerability triggering, intelligent vulnerability judgment and complex scene coverage can be realized.

The invention discloses an industrial control protocol intelligent fuzzy test method and system based on a multi-agent large model, and relates to an industrial information security test method and system.According to the industrial control protocol intelligent fuzzy test method and system based on the multi-agent large model, the large model is enhanced by integrating the field of the retrieval enhancement generation (RAG) and the low-rank quantization adaptation (QLoRA) technology, and a multi-agent cooperation mechanism is combined; and full-automatic, high-precision and end-to-end fuzzy testing of the industrial protocol is realized. The system generates seed data meeting protocol specifications through the seed generation agent, the test case generation agent executes field, structure and semantic variation based on the seed data, efficient test cases are dynamically generated, a test strategy is optimized in real time through the feedback analysis and strategy adjustment agent, and the vulnerability discovery capability is improved. By dynamically adjusting the variation strategy and optimizing the generation of the test case, the coverage, the accuracy and the efficiency of the test are improved, and the security and the vulnerability detection capability of the industrial control system are remarkably improved.

The invention provides a chip design and verification system and method.The system comprises a tool chain integration and automation module, the tool chain integration and automation module is provided with a tool containerization unit and a multi-physics field collaborative optimization unit, and the tool containerization unit is used for containerizing and packaging an EDA tool into an independent functional unit; each EDA tool container comprises a complete runtime environment, a dependency library and a license; the multi-physics field collaborative optimization unit is used for acquiring design parameters of the chip and evaluating the performance of the chip design scheme; the system further comprises a chip particle heterogeneous integration module which is used for carrying out system modeling and layout planning on the chip according to the received chip design file and carrying out multi-physics field simulation on a chip model. The system further comprises a security and credibility verification module which is used for carrying out vulnerability detection and security analysis on the chip design file in a credible environment in the chip design process. The invention further provides a working method of the system. According to the invention, the problem of isolation of a chip design tool chain can be avoided.

The invention discloses an asset vulnerability detection method and device, electronic equipment and a storage medium, and relates to the technical field of network security, and the method comprises the steps: actively sending a multi-protocol detection packet to scan a target network segment, and obtaining a first asset set; passively monitoring network traffic to extract asset feature information, and obtaining a second asset set to generate an asset list; port scanning tasks of all assets are dispatched to a plurality of scanning nodes in a distributed and parallel mode, dynamic port scanning is carried out according to a descending order of a plurality of key elements in combination with a port scanning optimization model based on risk prediction, and a full-amount port risk map is constructed; the static layer is matched with known vulnerabilities; the dynamic layer identifies suspicious behaviors deviating from a normal behavior baseline through an anomaly detection algorithm, and obtains an asset vulnerability detection result in combination with a cross validation method; according to the invention, the detection requirements of asset full coverage and early threat discovery in a complex network environment are met.

The invention discloses a binary vulnerability static mining method and system based on a large model, and relates to the data security and computer technology, and the method comprises the steps: extracting an external function from a decompiled binary file, recognizing a taint rendezvous point possibly causing a vulnerability by using the large language model LLM in combination with a rule base, and receiving an externally input taint source; generating an attacked target according to the calling point of the taint convergent point; performing reverse slicing based on the vulnerable target to generate a function call chain; screening the generated generation function call chain to obtain a dangerous flow; selecting an adaptive prompt template according to the type of the taint convergent point of the dangerous flow so as to perform block processing on the long function body and then perform combination to generate a prompt sequence; and utilizing the prompt sequence to guide LLM to perform vulnerability detection on the dangerous flow. According to the method, the accuracy, efficiency and automation level of binary program static vulnerability mining can be improved, and the defects of a traditional method in the aspects of complex dependency analysis and unknown vulnerability detection are overcome.

The invention discloses a security vulnerability detection method and device, a computer program product and a storage medium, and belongs to the field of code detection.A structured graph of a to-be-detected code can be constructed according to structured information of the to-be-detected code, and then the to-be-detected code is coded to obtain a text coding vector; and coding the structured graph of the to-be-detected code to obtain a structured coding vector, and finally inputting the comprehensive vector into a pre-trained detection model so as to obtain a security vulnerability detection result. The problem of how to reduce the omission ratio and the false alarm rate of security vulnerability detection of codes is solved, and the beneficial effects that security vulnerability detection is performed from two dimensions of semantics of code texts and semantics of structured information, and the omission ratio and the false alarm rate of security vulnerability detection of codes are reduced are achieved.

The present application belongs to the technical field of software engineering, and specifically relates to a multi-function homologous vulnerability detection method based on function selection and semantic equivalent matching. The method comprises two stages of signature generation and vulnerability detection. The signature generation stage comprises: inputting a vulnerability repair patch, extracting a modification method and a key repair method therein; performing semantic equivalent replacement on the extracted key repair method, and generating signatures for the functions before and after the semantic equivalent replacement respectively; the vulnerability detection stage comprises: inputting a target detection software, pre-processing by using a clone detection software to reduce the search space; extracting functions in the target detection software and performing semantic equivalent replacement thereon, generating signatures for the functions before and after the semantic equivalent replacement respectively, and performing two-stage signature matching, and finally outputting whether there is a suspicious multi-method homologous vulnerability in the target project. The method can help identify multi-method homologous vulnerabilities in open source software, reduce the harm caused by homologous vulnerabilities, and improve the security of open source software.

The invention relates to the technical field of vulnerability detection, in particular to a code-level security vulnerability detection method, electronic equipment and a storage medium. The method comprises the steps of obtaining a logic graph corresponding to a target code, wherein the logic graph comprises a role node, a service entity node, an operation edge and an access constraint edge; obtaining an application scene label of the target code according to the logic graph; inputting the target code into an AST resolver to obtain a syntax tree corresponding to the target code; obtaining a sensitive function library B and a suppression function library C matched with the target code according to the application scene label of the target code; and judging whether the target code has security vulnerabilities or not according to the syntax trees corresponding to the B, the C and the target code. According to the method, the unauthorized risk vulnerabilities in the program codes can be effectively identified.

A vulnerability detection and repair system utilize a classifier model to detect a software vulnerability in a source code snippet and the tokens in the source code snippet attributable to the vulnerability. A large language model is then given the vulnerable source code snippet, its vulnerability type, the vulnerability tokens, and a few-shot examples to determine whether or not the source code snippet includes the identified vulnerability. The few-shot examples include positive and negative samples of the type of vulnerability to guide the large language model towards the correct output.