491 results about "Vulnerability detection" patented technology

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 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 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 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 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.

In one implementation, a device identifies a first set of one or more Common Vulnerabilities and Exposures (CVEs) by searching a CVE database based on a request sent via a network towards a service. The device also identifies a second set of one or more CVEs by querying a large language model (LLM) based on the request. The device determines that the request is associated with a particular CVE based on the first set of one or more CVEs and the second set of one or more CVE. The device initiates a corrective measure with respect to the request in the network.

The invention provides an automatic vulnerability detection rule generation method based on LLM and static code analysis, and relates to the technical field of network security and software engineering. The method comprises the following steps of: constructing unified structured representation of project code semantics and vulnerability features, classifying taint'source 'and'sink' based on a heuristic rule and LLM (Logical Language Model), and constructing a taint propagation path assisted by a large language model and performing risk assessment; automatically generating a rule based on a'generation-verification-repair 'closed loop mechanism; according to the method, uniform structured representation of code side information and vulnerability side information is constructed, accurate identification of specific project taint sources and sinks and effective paths in multiple environments is achieved, a path risk sorting mechanism is introduced to achieve priority detection of high-risk paths, an automatic rule of error feedback circulation aiming at correction is established, and the accuracy of detection is improved. And the correctness and the performability of the rule grammar are greatly improved, and logic vulnerabilities are reduced, so that the manual debugging and maintenance cost is reduced.

The invention discloses a vulnerability intelligent collaborative detection method and system based on multi-agent reinforcement learning. The method comprises the following steps: independently executing detection according to local observation information and a strategy generation model through an intelligent agent deployed at an edge node; the strategy generation model is constructed based on a decentralized partial observable Markov decision process, and is obtained through cloud centralized training and global shared reward function optimization. When the agent obtains a forward reward, packaging a corresponding vulnerability context and related strategy parameters into an experience package; and the experience packet is diffused in the cooperative detection network in a mode of randomly selecting neighbor nodes and forwarding step by step. And after each agent receives the data, analyzing the content of the experience package and comparing the content with the local environment so as to dynamically adjust local strategy parameters and realize collaborative optimization. According to the vulnerability intelligent collaborative detection method and system based on multi-agent reinforcement learning provided by the invention, the problems that network security vulnerability detection is poor in expandability and lacks knowledge generalization ability are solved.

The invention relates to the technical field of vulnerability detection, can be applied to business scenes such as financial science and technology, medical health and the like, and discloses a dependency processing method, device, equipment and medium for a multi-component project, which comprises the following steps of: analyzing description files of different component types in a multi-language project, extracting dependency data to generate unified dependency description; the method comprises the following steps: constructing a panoramic dependency graph containing component nodes and dependency edges, identifying version constraints of the component nodes to generate a unified constraint model, determining a compatible version combination by processing the constraint model, extracting a multi-component operation environment configured with a tool during installation and operation of environment configuration data, and installing project dependency according to the compatible version combination. And scanning the panoramic dependency graph based on the vulnerability information source to generate a repair instruction for updating the vulnerability component version. According to the method, centralized analysis and collaborative management of multi-language dependency are realized through unified dependency description and constraint models, and conflicts are reduced and the consistency and security of the system are improved in combination with environment configuration and a loophole repair closed-loop process.

The invention discloses a contract vulnerability detection method and system based on cross-granularity feature fusion and meta-learning, and relates to the technical field of block chain security, the method comprises the following steps: obtaining a source code and a compiled byte code of a contract to be detected; source code semantic features are extracted from the source code, operation code execution features are extracted from the byte code, and graph structure features are extracted after a program dependency graph is constructed from the source code; performing cross-granularity attention interaction to generate cross-granularity fusion features; performing type-aware feature modulation on the cross-granularity fusion feature and a vulnerability type identifier of the current detection task, training a classifier by adopting a meta-learning strategy, and outputting a vulnerability detection result; and in response to the vulnerability detection result that the vulnerability exists, positioning the vulnerability code position based on the graph-source code attention weight matrix generated in the cross-granularity attention interaction process, thereby solving the problem of poor generalization ability in a rare vulnerability scene with scarce samples.

The invention discloses an interpretable vulnerability detection method based on large model enhanced graph learning, which comprises the following steps of: 1) performing sample denoising on training data, constructing a code attribute graph, and learning and training a vulnerability detection model based on a causal graph; 2) performing static analysis and feature extraction on a to-be-detected sample, and inputting the to-be-detected sample into the trained model for vulnerability detection; 3) performing context enhancement on the detected vulnerability sample to generate explanatory description information; according to the method, a real vulnerability mode is learned from a vulnerability sample with noise through sample denoising and a causal graph attention network model, meanwhile, a context-enhanced interpretation generation technology is put forward, high-quality vulnerability description is generated under the guidance of a retrieved similar sample to serve as interpretive information, and the vulnerability can be accurately identified. And the accuracy and interpretability of vulnerability detection are improved. The invention also provides an interpretable software vulnerability detection system based on large model enhanced causal graph learning.

The invention discloses an intelligent contract vulnerability detection method and system based on semantic comprehension and program path analysis, and belongs to the technical field of network security. The method comprises the following steps: firstly, analyzing an intelligent contract code by using a large language model, and reasoning to generate a structured security rule for defining a taint source, a taint sink and a purifier; secondly, guiding a taint analysis engine by using a security rule, and tracking on a data flow and a control flow diagram of a program so as to efficiently screen out a high-risk taint path; then, carrying out reachability verification on the high-risk path by adopting a symbolic execution technology; and finally, performing final context review on the verified vulnerability path by using the large language model again to generate a vulnerability report. According to the method, the semantic comprehension ability of the large language model and the preciseness of traditional program analysis are subjected to multi-stage cooperation, so that the detection precision and efficiency of the intelligent contract logic vulnerability can be remarkably improved, the interpretability of a report result is greatly enhanced, and the method has important application value.

Provided in the present application are a policy-based dynamic vulnerability blocking method and apparatus, and an electronic device. The method comprises: on the basis of a dynamic vulnerability detection mechanism, acquiring a binary-level security policy, wherein the binary-level security policy comprises sensing-point-related information, decision-point-related information, constraint information and execution unit information; and on the basis of the binary-level security policy, detecting a potential attack present in a currently running program, and executing a target operation, so as to promptly block vulnerabilities and thus ensure the normal operation of the currently running program. In the method, by means of automatically generating, on the basis of a dynamic vulnerability detection mechanism, a binary-level security policy and executing same, effective protective measures can be provided for active programs and kernels having vulnerabilities before the vulnerabilities are patched, and such a process does not require developers to have professional security knowledge nor require spending a large amount of time on manual analysis.

The invention provides a vulnerability detection method, electronic equipment, a storage medium and a computer program product, and relates to the technical field of computers. The method comprises the following steps: acquiring a code semantic map of a to-be-detected item; performing test analysis on the to-be-detected item to obtain a vulnerability detection path of the to-be-detected item; and sending a vulnerability detection request to the large language model. And if missing element information returned by the large language model for the vulnerability detection request is received, matching nodes in the code semantic map based on the missing element information. And for each code element in the missing element information, determining a target node from the matched node based on the semantic association degree between the node matched with the code element and the vulnerability detection path, thereby sending a new vulnerability detection request to the large language model. By implementing the method and the device, the code context information can be accurately provided when the big language model is adopted for vulnerability detection, and the vulnerability detection accuracy is improved.

The invention discloses an intelligent contract vulnerability detection method based on hierarchical multi-granularity coding, and the method comprises the steps: dividing an intelligent contract source code into multi-level data for feature extraction, and determining a token feature corresponding to each token, a statement block feature corresponding to each statement block, and a cross-statement block feature corresponding to each code segment; according to the token feature of the token and the statement block feature corresponding to the statement block, determining an intra-segment feature corresponding to each code segment, according to the cross-statement block feature, determining a cross-segment feature, and according to the intra-segment feature, the cross-segment feature and the document-level feature, determining a vulnerability detection result, therefore, a unique hierarchical structure is divided according to the code features so as to facilitate feature extraction of the vulnerability detection model, and an interaction relationship and a dependency relationship among the token, the statement block and the code segment can be obtained through feature extraction and feature fusion of the vulnerability detection model, so that the accuracy of intelligent contract vulnerability detection is improved.

The invention relates to the field of computer security, in particular to an intelligent substation logic vulnerability protection method and system based on business rule objectization, and the method comprises the steps: obtaining an unstructured preset business rule of an intelligent substation, and carrying out the objective processing of the preset business rule, and obtaining a standardized rule set; constructing a security agent strategy library based on the standardized rule set; collecting real-time business data and equipment state data in the operation process of the intelligent substation to form context information during operation; and performing matching verification on the runtime context information and the security agent strategy library to obtain a vulnerability detection result, and executing a corresponding vulnerability protection response operation according to the vulnerability detection result. According to the method, deep defense of legal-structure and malicious-logic vulnerabilities which cannot be defended by traditional means is realized, automation accuracy, low performance overhead and dynamic adaptability during business rule updating are also realized, and safe operation of the intelligent substation is effectively guaranteed.

The invention belongs to the field of information network security. The invention specifically discloses an agent-based logic vulnerability detection method, system and device, and a medium. The method comprises the following steps: capturing service system request response data through network traffic, identifying an HTTP protocol through a port and protocol features, filtering non-HTTP request data, extracting and analyzing the request data, standardizing extracted parameters, continuously collecting and analyzing through an API analysis agent, improving the semantic analysis capability of a large model, perfecting a logic relation graph, and improving the semantic analysis efficiency of the large model. A complete vulnerability life cycle data link is constructed through a vulnerability analysis agent, the system logic vulnerability mining efficiency and coverage are improved, and automatic logic vulnerability alarm and disposal can be realized and the risk disposal real-time performance is improved by accessing the agent to a network security protection product, an alarm analysis platform and other systems.

The invention discloses a method and a device for generating a software vulnerability analysis report, and relates to the field of information security. The method comprises the following steps: acquiring auditing information of software vulnerabilities; a code feature matching rule for the software vulnerability is generated according to the auditing information through the large language model, and the code feature matching rule is used for matching a code calling mode related to the software vulnerability in the target software project; constructing a calling graph of the target software project; according to the code feature matching rule and the call graph, the correlation degree between each code snippet of the target software project and the software vulnerability is detected, and the correlation degree is used for evaluating the risk level of each code snippet of the target software project; and generating a software vulnerability analysis report of the target software project according to the association degree between each code snippet of the target software project and the software vulnerability. The technical problem that in the prior art, an open source software vulnerability detection tool cannot accurately evaluate the real influence of vulnerabilities in specific project contexts is solved.

The invention relates to the cross technical field of artificial intelligence and software security testing, in particular to a deep learning framework fuzz testing method based on large model cue word optimization, which is used for improving vulnerability mining efficiency and testing intelligence level of fuzz testing on a deep learning framework. According to the method, the advantages of a large language model in the aspects of code understanding and generation are fully utilized, and efficient vulnerability detection of a deep learning framework is realized by introducing a cue word adaptive optimization and variation mechanism. The method mainly comprises the following steps: (1) providing a deep learning framework API classification method and a cue word routing mechanism; (2) proposing a large model cue word adaptive optimization mechanism; and (3) proposing a deep learning framework fuzzy test variation strategy and a dynamic selection mechanism. According to the method, the automation and vulnerability discovery capability of fuzzy testing can be remarkably improved while the generation quality is ensured, and the method has relatively high universality and application value.

The invention discloses an automatic security vulnerability detection method and system for rail transit signal system equipment, and the method comprises the steps: carrying out the unpacking and analysis of firmware of to-be-detected rail transit signal system equipment, so as to recognize a target file in the firmware; performing security attribute verification on the target file based on a preset rail transit security attribute rule base, including performing state consistency verification on a plurality of associated parameters in the control logic to identify security vulnerabilities caused by state conflicts; performing time sequence attribute verification on the time sequence sensitive security operation to identify security vulnerabilities caused by violation of the real-time constraint; and performing risk assessment and outputting a vulnerability detection result based on a security attribute verification result. Compared with the prior art, the method has the advantages that the formal rail transit safety attribute rule base is constructed, state consistency verification and time sequence attribute verification are carried out on the control logic in the equipment firmware at the same time, and automatic mining and risk assessment of deep business logic vulnerabilities are achieved.

The invention discloses a privacy protection and robustness test method and system for large model fine tuning, and belongs to the technical field of machine learning security. The method comprises the steps that a three-layer distributed architecture comprising an edge server, a cloud server and a plurality of edge clients is constructed, the edge clients distill local privacy data and cooperate with the edge server to train a global model, and a candidate detection sample set is formed; screening a sample set based on the potential feature deviation evaluation index, and sending the sample set to a cloud server for vulnerability detection to obtain an optimal backdoor detection candidate sample set; and multi-trigger parallel and progressive trigger sequence backdoor implantation is respectively used for scenes of single fine tuning and multiple fine tuning of the large model, an optimal backdoor detection candidate sample set is combined with a preset trigger to generate a backdoor test sample set, the backdoor test sample set is mixed with a clean data set, and then the robustness of the backdoor test sample set is tested through fine tuning of the large model. Large model fine tuning and robustness testing of privacy protection can be realized in a heterogeneous model cooperative training environment.

The invention provides a terminal vulnerability detection method and system based on cooperation of a multi-modal graph and a physical rule, and relates to the technical field of intelligent network connection automobile security, and the terminal vulnerability detection method comprises the steps: constructing a vehicle-mounted multi-modal graph structure containing four-dimensional heterogeneous features through analyzing a vehicle-mounted terminal; performing embedded learning on the vehicle-mounted multi-modal graph structure with the four-dimensional heterogeneous features through a lightweight graph neural network to generate a unified feature vector; inputting the feature vectors into a deep learning detection module to output vulnerability category probability distribution, activating a dynamic rule inference engine under the condition that the output maximum probability exceeds a preset threshold value, executing rule base verification based on vehicle physical constraints, and if logical inference verification is passed, confirming vulnerabilities and outputting a rule violation report; and constructing a vehicle-mounted exclusive vulnerability knowledge graph through vulnerabilities and association rules. And the detection accuracy of the vehicle-mounted vulnerabilities is improved by constructing the multi-modal diagram and carrying out cooperative detection on the physical rules.

The invention relates to the technical field of software security, and particularly discloses a real-time vulnerability detection and repair system of an AI code generation model. The system comprises a central coordinator, a real-time monitoring module, a deep analysis module, an intelligent repair module and a feedback learning module. The real-time monitoring module rapidly scans and triggers the generated codes; the deep analysis module performs interprocess stain analysis on the trigger code to generate a diagnosis report containing a precise data flow path; the intelligent repair module generates a repair patch under the function equivalence constraint according to the diagnosis report; and the feedback learning module collects the processing feedback of the developer on the repair patch, and drives the continuous optimization of each module of the system. According to the method, through layered progressive analysis and repair generation, the repair accuracy and the function safety are ensured; through a feedback learning mechanism, the self-adaptive evolutionary ability of the system is realized, the problems of unavailability of repair suggestions and static stiffness of the system in the prior art are solved, and real-time protection is provided for AI auxiliary programming.

The present application provides a system and a method for detecting an Apache HTTP Server path traversal vulnerability. The method comprises: inputting a target IP address list into a scanning module, and probing the network reachability of each IP address in the target IP address list; if there is a network-reachable IP address, probing all ports of the IP address, and recording open port numbers; identifying services running on the open ports and performing service classification; if a service is classified as an Apache service, initiating a Curl request command to an icons directory path of the Apache service; if the Curl request succeeds and an / etc / passwd file is read, parsing the / etc / passwd file and extracting users of a host under test; performing detection on the users of the host to obtain an Apache HTTP Server path traversal vulnerability; and generating a detection report for the host on the basis of the Apache HTTP Server path traversal vulnerability.

The invention relates to the field of software vulnerability detection, and discloses a software vulnerability detection method based on code standardization and cross-modal semantic collaboration, and the method comprises the steps: obtaining a to-be-detected source code, inputting the source code into a trained software vulnerability detection network for detection, and then outputting a software vulnerability detection result. The software vulnerability detection network detects an input source code to be detected, and comprises the following steps: S1, code standardization; s2, performing semantic and structure dual-path feature extraction; S3, performing cross-modal semantic collaboration; and S4, inputting the multi-modal code representation into a classifier, and outputting a probability value that the source code to be detected has the software vulnerability. According to the method, interference caused by grammar differences and coding styles can be effectively relieved, deep fusion of semantic information and structural features is achieved, and the accuracy and robustness of software vulnerability detection are remarkably improved.

The invention provides a vulnerability discovery method and system based on symbol-semantic hybrid reasoning, and belongs to the technical field of software security. The method comprises the steps that a target program is analyzed, and an event sequence is extracted; converting the event into a symbol with a time sequence label and confidence, and constructing a symbol dependency graph; reasoning based on the dependency graph, calling a large language model to generate a semantic reasoning action when the certainty is insufficient, and updating the state after symbol verification; new rules are extracted from the inference chain passing verification through reinforcement learning, and self-evolution is achieved; and outputting a vulnerability report containing the reasoning path and the confidence coefficient. The system correspondingly comprises a time sequence probability symbol module, a symbol-semantic bidirectional coupling reasoning module, a reinforcement learning self-evolution module and a report generation module. According to the method, the preciseness of symbol logic and the generalization ability of a semantic model are fused, the accuracy, interpretability and adaptive ability of vulnerability detection are effectively improved, and the method is suitable for security audit and code review of a complex software system.