547 results about "Penetration test" patented technology

Advanced cybersecurity systems and methods may provide enhanced security of critical infrastructure without the risks associated with traditional penetration testing. The systems and methods may model network vulnerabilities and simulate attack scenarios to identify potential security breaches. This supports the integration of generic rules and common properties, allowing for dynamic and scalable vulnerability analysis across complex network systems. Additionally, the systems and methods incorporate a multi-purpose fuzzer and an AI-driven Cyber Reasoning System (CRS) that autonomously detects and remedies software vulnerabilities based on predefined logic and pattern recognition. This comprehensive approach identifies existing vulnerabilities and predicts potential future threats by analyzing changes in network behavior and data flow. The systems and methods are particularly suited for mission-critical environments where operational continuity is paramount, providing a robust tool for improved cybersecurity management without disrupting system operations.

The invention provides a penetration test agent system driven by a large language model and a test method, and the system comprises a memory unit which is composed of a penetration test knowledge base, comprises data elements including vulnerability information, a penetration mode and tool information, and employs a knowledge graph to establish a vulnerability knowledge representation model; the intelligent unit is realized by a large model and is used for generating a tool calling instruction, a penetration test load and a penetration test strategy based on the memory unit; and the planning unit is used for splitting the penetration test task into a task planning state machine consisting of controllable sub-tasks so as to guide the large language model to execute a specific task. Through introduction of the large model, the knowledge base and the task planning unit, the automation and intelligence level of the penetration test task is improved; the integrity of knowledge retrieval is improved, and the interpretability and consistency of large model output are ensured; for a complex scene of a penetration test process, a highly customized proxy behavior is realized, and a penetration test task execution process is guaranteed.

The invention provides a security data penetration test method. The method comprises the steps of pre-establishing a multi-thread application program race condition vulnerability feature library, performing static analysis on source codes of a target application program, identifying potential race condition vulnerability points, and obtaining a preliminary vulnerability candidate set; for monitored suspected race condition events, abstracting event context information into constraint conditions, exploring an input combination of suspected static conditions by using a constraint solver, and determining a minimum input set of stable triggering race conditions; aiming at memory models and synchronous primitives of different programming languages, obtaining a race condition inverse mode, and extracting universal utilization primitives covering various race condition vulnerabilities; in the penetration test process, static analysis, dynamic monitoring and symbolic execution are applied to mine race condition vulnerabilities of a target application system, the availability of the vulnerabilities is verified, and evaluation of system security is assisted to be completed.

The invention discloses an automatic penetration test method, system and device based on deep reinforcement learning, and a medium, and the method comprises the steps: carrying out the vulnerability scanning and baseline verification of a device through employing a vulnerability scanning and baseline verification tool, and obtaining the vulnerability information of a tested system; generating an attack graph according to the obtained vulnerability information, determining a vulnerability utilization probability according to the vulnerability information, and assigning values to edges of the attack graph to obtain a Bayesian attack graph; designing an attacker target node reward value on the generated Bayesian attack graph, simulating a possible attack behavior of an attacker, and calculating an attack strategy of the attacker by using a deep reinforcement learning method; simulating potential attack behaviors of an attacker in the attack graph according to an attack strategy, and generating a penetration attack path which is most likely to be adopted by the attacker; and performing an automatic penetration test to generate a penetration test report. According to the invention, the penetration test scheme can be intelligently generated according to the vulnerability of the system, and the precision and efficiency of network security evaluation are greatly improved.

The present application provides a network testing method and apparatus, an electronic device, a storage medium, and a program product. The method comprises: obtaining target network node information of a target network node, and determining a virtual network topology on the basis of the target network node information; by using a vulnerability knowledge graph, determining target patch information and target vulnerability information corresponding to the target network node; on the basis of preset network node rewards and attack operation success rates, performing a virtual network penetration test on the virtual network topology where the target network node is located, so as to obtain a penetration test result; on the basis of the penetration test result, determining an exploited network node, and on the basis of the vulnerability knowledge graph, determining remediation patch information, so as to remediate the exploited network node by using the remediation patch information; performing a virtual network penetration test on the exploited network node and the target network node, so as to obtain a post-remediation penetration test result; and on the basis of a comprehensive score corresponding to the post-remediation penetration test result and a comprehensive score corresponding to the penetration test result, respectively obtaining a vulnerability report and a defense report for the target network node.

The invention discloses an unmanned inspection and intelligent fault judgment method. A security threat model is constructed through deep learning, potential risks are identified, a strategy is made, security analysis modeling based on a comparison technology is utilized, and system state abnormity is detected. In data transmission, confidentiality and integrity are guaranteed by integrating a plurality of security means, a multi-level protection framework is designed, and all-round defense is carried out from a network boundary, a host terminal and an application system. By means of anomaly detection monitoring index data, anomaly and risk are subjected to correlation analysis, and a basis is provided for decision making. And regularly carrying out security assessment and penetration testing, repairing vulnerabilities, and carrying out optimization and reinforcement. The method covers multiple aspects such as threat modeling, safety analysis, data protection, protection framework construction, anomaly detection and evaluation reinforcement, the safety and reliability of the unmanned inspection system are effectively improved, network attacks and illegal access are resisted, and stable operation of the system is guaranteed.

The invention relates to the technical field of automobile network security, in particular to an intelligent networked automobile penetration test path generation method and system based on a knowledge graph, and the method comprises the steps: constructing an intelligent networked automobile intranet penetration test security knowledge graph through fusing multi-source heterogeneous data based on an intranet E / E architecture; generating a potential attack path based on the knowledge graph by using a depth-first search algorithm of dynamic pruning optimization; a multi-dimensional space-time risk assessment framework is utilized to quantify path threat intensity and execution feasibility, and quantitative assessment on the generated attack path is realized; and according to an evaluation result, selecting a high-risk and feasible attack path to carry out an actual penetration test, and discovering security vulnerabilities and weaknesses in the system. According to the method, the attack surface is fully covered, the high-value attack path is efficiently generated, the threat intensity and the execution feasibility are accurately and quantitatively evaluated, and the safety test efficiency and accuracy of the intelligent network connection automobile intranet are remarkably improved.

The invention discloses an information scanning and penetration test path planning method in a Windows domain based on reinforcement learning. The method comprises the following steps: carrying out information detection on a target Windows domain environment, surveying and mapping a network topology of the target Windows domain environment, establishing connection with a target machine by utilizing protocols such as LDAP (Lightweight Directory Access Point) and the like, and collecting related information in the domain; the method comprises the following steps of: collecting, sorting and abstracting detected related information, solving a multi-stage sequence decision problem by utilizing a Markov decision process, realizing environment simulation, constructing a reinforcement learning agent, generating a penetration test path by performing interactive training with a simulation environment, and performing adjustment according to a reward strategy so as to obtain a penetration test result. And searching an optimal penetration test path by accumulating rewards, constructing a Windows domain penetration test model, and finally generating the optimal penetration test path. According to the method, collection of information in the Windows domain and planning of the penetration test path are realized, and finally, the environmental safety of the Windows domain is evaluated and maintained.

The invention discloses a method based on a large language model and ATTamp; the invention discloses a CK framework penetration test automation method and device, and the method comprises the steps: firstly carrying out the structural analysis of multi-source input information and tool output, and guaranteeing that key fields are not discarded; then combining a retrieval enhancement generation technology and a network security knowledge base to provide domain knowledge support for the large language model, so as to generate a model with ATTamp; a penetration test task tree marked by CK tactics, technologies and sub-technologies; on the basis, an optimal tool is automatically selected through a tool resource library and a multi-dimensional screening mechanism, an execution instruction is generated, and finally an execution result is returned to the input analysis module to form a self-adaptive optimization test closed loop. According to the method, semantic fidelity compression and standardization processing of long information can be realized aiming at the problems of large output format difference, more information redundancy and the like of different penetration testing tools, and efficient, explainable and auditory technical support can be provided for automatic penetration testing in a complex network environment.

The invention provides an attack fault tree security risk assessment system based on deep reinforcement learning, and relates to the technical field of information security. The system specifically comprises a network asset data fingerprint scanner module, a deep reinforcement learning network model generation and training module and an automatic penetration test tool set module. The invention aims to determine the optimal attack path from the system attack graph by using deep reinforcement learning. By combining the screened attack paths with various resources and time costs encountered in the actual penetration test, the failure rates of various security risks in the attack fault tree, the success probabilities of various attacks and other indexes can be determined. And finally, establishing an attack fault tree of a power system scene in the embodiment, comparing the attack lethality and the detection capability level of each attack path in a game phase, and qualitatively and quantitatively analyzing the final security capability level of the target system and the damage degree caused by attacks.

The invention provides an intelligent penetration testing system based on deep reinforcement learning and large language model hierarchical decision, and belongs to the technical field of penetration testing. The system comprises a macroscopic action decision model, an action library, a state information encoder, a target selector, a downstream task processing model and a tool library, wherein the target selector selects a to-be-tested target according to a detection result of a target network environment; the macroscopic action decision model uses a deep reinforcement learning algorithm to receive a state vector of a target environment, and the state vector is determined according to ATTamp; the CK framework selects and outputs a penetration test task to be completed in the next step from the action library, and the output action serves as a downstream task target and is issued to a downstream task processing model; and the downstream task processing model is a large language model, autonomously generates a solution according to the task description and calls the penetration test tool in the tool library for execution, a feedback result after task execution is observed and recorded by the large language model, and whether the downstream task is completed or not is judged by the large language model.

Systems and method for detecting vulnerabilities in code are provided herein. A pre-trained artificial intelligence (AI) model is engaged, and a plurality of prompts and the source code are provided to the AI model. A plurality of detected vulnerabilities and a plurality of code locations in the source code are identified using the AI model. Each of the plurality of code locations corresponds to at least one of the plurality of detected vulnerabilities. One or more false positive vulnerabilities in the plurality of detected vulnerabilities are identified. A plurality of augmented prompts is generated, based on the one or more false positive vulnerabilities. The plurality of augmented prompts is outputted to a database of prompts for use in future code analyses, without necessarily having to retrain the AI model.

The invention provides a network penetration testing method and system based on state feedback, and belongs to the field of computer security detection.The method comprises the steps that vulnerability information is collected, and a vulnerability database is constructed; performing vulnerability attribute identification according to the vulnerability database, and matching penetration test conditions based on an identification result; matching the penetration test condition with a preset penetration test condition template for the target environment through LLM, if matching succeeds, judging that the target environment meets a vulnerability utilization condition, selecting a test condition meeting the vulnerability, and starting a corresponding penetration test process; executing a penetration test according to the generated test plan by using a state machine, and judging the test effect and the accuracy of state conversion through a feedback mechanism; and according to test feedback, the rule or condition template in the matching process is further optimized. The method does not need to preset a large number of rules and test cases to match the state, and can judge the test effect and the accuracy of state conversion through a feedback mechanism.

The invention provides an efficient multi-dimensional network penetration testing method based on RAG, which comprises the following steps: firstly, identifying sub-domain names possibly existing in a target website, and sequentially expanding attack surfaces of penetration testing to obtain vulnerability information pairs; secondly, a design model generated based on retrieval enhancement is adopted, vulnerability information pairs are extracted from the queue to be utilized, knowledge items related to local knowledge base retrieval and network intelligent search retrieval are utilized, and finally, a large model generates vulnerability utilization information according to the knowledge items; acquiring target machine permission for the previously acquired vulnerability information pair construction command injector, and further scanning other hosts of the intranet accessed by the target skipping machine; finally, combining vulnerability information obtained by penetration testing, utilizing a large model to sort vulnerabilities existing in different assets, and outputting penetration testing reports for different assets. According to the method and the system, comprehensive penetration testing of cross-network and cross-system is realized, security experts are helped to quickly identify, verify and repair vulnerabilities in a complex and changeable network environment, and high-efficiency and low-cost network security maintenance is realized.

Aspects described herein may automatically generate threat models using large language model (LLM). A computing device may send, to LLM, one or more software modules associated with a computing system. The computing device may request the LLM to generate a threat model of the computing system. The computing device may receive, from the LLM, a first output based on the first prompt comprising first information for a first version of the threat model and a penetration test script for the computing system. The computing device may input, to the LLM, the result of the penetration test together with the LLM's previous output, to facilitate the LLM to generate a refined version of the threat model.

The invention provides a Web automatic penetration test method and device, electronic equipment and a storage medium, and belongs to the technical field of website vulnerability tests.The method comprises the steps that a web crawler collects target Web information to form a basic set; detecting vulnerability acquisition information; matching the adaptive script, and generating an intelligent load to verify vulnerability availability; constructing a knowledge graph to determine vulnerability association, and evaluating success rate and risk; planning an optimal attack route by using reinforcement learning, and determining attack depth and range, namely attack path risk features; and evaluating the risk by the dynamic scoring model, and generating a visual report containing detailed information, results and repair and improvement suggestions. According to the automatic penetration testing method, manual intervention is reduced in the whole process, dependence on professional experience is reduced, efficiency is improved, and the problems that traditional testing is tedious and low in efficiency are solved.

A system is provided for intelligent automated simulation of penetration testing and isolation of vulnerable distributed electronic data registers. In particular, the system may extract metadata regarding one or more nodes or sections of a distributed register. Based on the metadata, the system may generate a knowledge graph that may indicate the vulnerabilities associated with particular nodes, blocks, and / or sections of the distributed register. Based on the knowledge graph, the system may compute vulnerability scores for the various nodes, blocks, and / or sections, and generate a vulnerability heatmap based on the scores. The system may further be configured to allow a user to perform automatic simulated penetration tests on the vulnerable portions of the distributed register and / or execute one or more remediation process on such vulnerable portions. In this way, the system provides an efficient way to identify and remediate vulnerabilities within a distributed register.

The invention relates to a data security protection verification method and system for environment field monitoring equipment, and belongs to the technical field of environment field monitoring. The method comprises the following steps: acquiring evaluation data of a positioning and time service accuracy index; performing penetration test on the interface and the storage medium of the environment field monitoring equipment to obtain evaluation data of sealing safety indexes of the interface and the storage medium; acquiring evaluation data of the database encryption protection indexes by adopting a security scanning tool and an encryption analysis tool; simulating the operation scene, and obtaining evaluation data of the running operation log record index; and automatically judging the compliance of the data based on the convolutional neural network model, and generating a data security protection level quantitative evaluation result of the environment field monitoring equipment based on the evaluation data of each index in the multi-dimensional quantitative evaluation index set. According to the environment field monitoring equipment data security verification method and system, the monitoring equipment data security protection level can be quantified, and monitoring data security improvement is promoted.

The invention relates to the technical field of vehicles, in particular to a vehicle information safety production consistency detection method and device and electronic equipment.The method comprises the steps that a target part injected with first secret key information is received, black box testing is conducted on the target part, and a black box testing result is obtained, and if the black box test result meets a first preset test condition, performing a white box test on the target part to generate a white box test result of the target part, and if the white box test result meets a second preset condition, generating safety test information of the target part. Therefore, the problem that the production consistency of part information safety cannot be guaranteed due to the fact that whether the debugging port of the part is effectively closed or not cannot be verified through a detection method in the related technology is solved, effective closing of the debugging port is verified through the method of carrying out secret key injection and penetration testing on the part, and therefore the production consistency of the part information safety is guaranteed.

The invention is suitable for the technical field of payment data system vulnerability assessment, and provides a payment data security assessment method and system.The method comprises the steps that after vulnerability scanning and penetration testing operation is conducted on a payment data storage system, a plurality of scanned security defects are determined, and acquiring a preliminary risk priority evaluation index allocated to each security defect, and acquiring a historical operation log of the payment data storage system. According to the method, the mechanism of double correction factors is introduced, so that the repair priority evaluation of the potential risk vulnerabilities in the payment data storage system is optimized. Firstly, in combination with a hysteresis effect correction factor and a secret key updating frequency deviation correction factor, the accuracy and timeliness of vulnerability repair are effectively improved;

The invention discloses a graph structure-based domain penetration attack path generation method, which belongs to the technical field of network security, and comprises the following steps of: constructing a multi-level relation graph comprising a host node, a service node and a user node through automatic detection by taking any host in a domain as a starting point; assigning a weight attribute to the atlas edge based on a vulnerability library and an attack pattern library; an improved heuristic graph search algorithm is adopted, all feasible attack paths and threat scores thereof are generated by integrating the path length, attack difficulty and permission improvement effect, and the problem that the threat scores of all the feasible attack paths are influenced in various scenes and dynamic change domain environments is solved. The technical problems of realizing comprehensive automatic penetration testing, accurately identifying potential attack paths and establishing a systematic threat assessment mechanism are solved, the automation, intelligence and high-efficiency level of domain penetration testing is remarkably improved, and the method has good adaptability, expansibility and practical value and is suitable for popularization and application. And attack path discovery and risk early warning work in a dynamic network environment with high security requirements can be effectively supported.

The invention discloses a multi-model collaborative penetration testing method and device and electronic equipment, and relates to the field of data processing. In the method, target asset information and external knowledge data of a target system are obtained, and the target system is a to-be-permeated test object; analyzing the target asset information in combination with external knowledge data to obtain corresponding structured penetration data; obtaining the current state of the target system, inputting the current state and the structured penetration data into a preset large language model, and generating a penetration test strategy; based on the penetration test strategy, adopting a preset reinforcement learning algorithm to generate an attack path; the attack path corresponds to the original; inputting the attack code into a preset generative adversarial network to generate a confusion attack code; and performing penetration test on the target system based on the attack path and the obfuscated attack code to obtain a penetration test result. By implementing the technical scheme provided by the invention, the penetration test coverage rate is improved.

The invention relates to an automatic penetration testing method based on reinforcement learning, and belongs to the field of computer and information science and technology network space security. The method comprises the following steps: firstly, constructing a state space, an action space and a reward function of reinforcement learning by utilizing parameters related to the environment to generate model training data; secondly, applying training data and vulnerability utilization component training to generate a reinforcement learning model; and finally, based on the vulnerability utilization component library, preferential vulnerability utilization information is recommended through a reinforcement learning model, and automation of the whole process of penetration testing is realized. The artificial intelligence technology is adopted for solving the problems of manual dependence of penetration testing, application solidification of the vulnerability utilization component and the like, the recommendation accuracy of the vulnerability utilization component is effectively improved, and the efficiency of penetration testing is remarkably improved.

The invention discloses a concrete waterproof coating performance testing method and device, belongs to the technical field of waterproof material performance detection, and aims to improve and optimize a sample detection mode and a detection method based on a conventional water pressure penetration testing principle. Samples forming an annular structure can be conveniently placed in a detection cylinder, multiple groups of waterproof coatings on the samples correspond to multiple detection cavities in a one-to-one mode, multiple samples can be detected in the same group, forward water pressure testing from outside to inside and then reverse water pressure testing from inside to outside are adopted, and the samples do not need to be replaced in the testing process; in the successive pressurization gap of the positive and negative water pressure test, the waterproof coating is repeatedly dried through the heating plate, the repeated waterproof environment of the waterproof coating in the actual engineering is simulated, and the detection accuracy is improved.

The invention discloses a medical network collaborative penetration test system and method based on artificial intelligence, and belongs to the technical field of medical information security, and the method comprises the steps: integrating a hospital intranet, medical equipment, cloud data center data and historical network delay data to construct a medical network data center; analyzing association between network vulnerabilities and delay data through a graph neural network, constructing an attack path analysis model containing delay weights, and quantifying risk scores to generate a test target priority list; defining a protocol vulnerability and a delay threshold, designing a test scene and generating an attack vector, matching an attack tool to perform a simulation test, calculating a vulnerability triggering success rate and evaluating a risk; generating a penetration test case according to the test target priority list and the risk level, executing multiple rounds of attack tests and generating a report; and monitoring the change of the vulnerability triggering success rate by dynamically adjusting the delay parameter, setting an early warning threshold value, and monitoring and sending a risk early warning notification in real time.

The invention provides a penetration test system and method based on multi-agent collaboration, and the system comprises an interaction module, and a plan generation agent, a plan planning agent and a plan execution agent which comprise a large language model LLM, and the interaction module is used for receiving a to-be-solved problem inputted by a user; the plan generation agent is used for generating a penetration test task plan according to a to-be-solved problem; the plan planning agent is used for splitting the penetration test task plan to generate a plurality of sub-plans; a reinforcement learning technology is utilized to screen out an optimal adaptive API of an optimal tool corresponding to the current plan description, parameters required by the current API are extracted from the subtasks, and a final penetration test command is generated; and the plan execution agent is used for receiving the penetration test command transmitted by the plan planning agent, executing the command and recording a generated execution result. According to the method, a user only needs to give a penetration test target and purpose, a penetration test scheme can be automatically given, a penetration test task is carried out, and a result is finally given.

The invention discloses an automatic penetration method and system based on generative adversarial imitation learning in a cloud environment, and the method comprises the steps: storing an execution action-state pair when a reinforcement learning model successfully penetrates a target drone through an Exploit open source tool and a Metasploit open source penetration tool, so as to construct a penetration expert knowledge base; inputting the content in the expert knowledge base into a discriminator in the imitation learning framework for training; training actions output by the rear discriminator by using an imitation learning framework, wherein the actions can be used for automatically infiltrating a specified host; and deploying the whole automatic penetration framework to a cloud environment through containerization and arrangement to simulate external network attacks. According to the method, imitation learning is carried out in the cloud environment, resources can be efficiently utilized, container resource allocation can be automatically adjusted by enhancing the GAIL network and penetration test tool containerization, and therefore the overall performance is improved, and resource use and response time is optimized.

The invention discloses a graph-driven self-attention compressible memory management method, and belongs to the technical field of penetration testing. According to the graph-driven self-attention compressible memory management method, an attention graph is constructed, semantic dependence and attention flow directions among multiple Agent nodes are accurately described by utilizing edge weights, the relevance and the accessibility of information are improved, and in the aspects of screening and loading, the expandability of the memory is improved. According to the method, key memory fragments are screened on the basis of concerned graph edge weights and task related features, only the loaded information is loaded, redundancy is effectively eliminated, the data volume processed by a system is reduced, and the system operation efficiency is improved; the semantic compression module can perform abstract processing on historical information based on various elements, and on the premise of ensuring that key semantic information is not lost, the abstract length is controlled within the window limit, so that the model can normally process information, and the adaptability and stability of the system to different data volumes are improved.

A system for automated penetration testing using artificial intelligence (AI) is described, which aims to enhance cybersecurity by simulating attacks on software and computer systems in order to measure and identify vulnerability. The platform employs AI agents to perform tasks including script execution for vulnerability testing, social engineering simulations, comprehensive security assessments, and more, thereby reducing costs, time, potential risks associated with traditional penetration testing methods, and providing a more complete and available solution than humans can produce alone.

Systems and methods for testing, evaluating, and scoring IT products (e.g., software) and product updates from a technology provider are disclosed herein. More specifically, organizational assessment may be performed to evaluate the provider's development lifecycle processes and generate organization maturity scores. Architecture assessment may be performed to evaluate the system-level and software-level architectures associated with the application and generate architecture maturity scores. Product verification may be performed via automated testing and penetration testing to generate verification maturity scores. The organization maturity scores, architecture maturity scores, and verification maturity scores may be used to provide recommendations to the provider and also combined into an overall maturity score, which may serve as a comprehensive summary of the evaluation. These generated scores inform and expedite testing of future iterations of the product.