419 results about "Security bug" patented technology

An AI-based Cybersecurity System and Method enable real-time detection, analysis, and mitigation of cyber threats within computing networks using adaptive artificial intelligence. The system continuously monitors network traffic, extracts behavioral and contextual attributes, and applies deep learning-based inference to identify anomalous activities indicating security breaches. The method integrates several computational units, including a network monitoring unit, feature extraction unit, artificial intelligence processor, contextual reasoning processor, and decision synthesis unit, to compute a composite risk index quantifying threat likelihood and severity. A classification processor categorizes detected threats into types such as ransomware, phishing, or unauthorized access, while a mitigation control processor initiates automated response actions to isolate compromised nodes and restore network integrity. An adaptive learning processor updates AI models using feedback from confirmed incidents. This provides a scalable, self-evolving cybersecurity framework that minimizes human intervention and enhances resilience against dynamic and zero-day threats.

The invention discloses a streaming knowledge injection and adversarial self-optimization large language model training method and system, and the method comprises the steps: collecting the newest knowledge of an authoritative information source in real time, converting the newest knowledge into a structured constraint rule through a semantic analyzer, and dynamically updating a knowledge base; based on the updated knowledge base, adopting a PPO algorithm to optimize a generator, actively constructing a high-risk adversarial sample, and forcing the model to expose security vulnerabilities; after user input and adversarial samples are input into the model, total loss is calculated through constraint detection, gradient updating is blocked if the total loss exceeds a threshold value, and otherwise, a multi-level safety verification stage is started; and finally, fusing a security verification result and the adversarial loss, updating a total loss function and cooperatively adjusting model parameters to form a continuous self-optimization training cycle. According to the method, compliance is guaranteed through streaming knowledge injection, vulnerabilities are actively mined in combination with adversarial training, verification precision is improved by means of multi-level detection and dynamic threshold adjustment, and safety and reliability of a large language model in a complex scene are enhanced.

Techniques are described for techniques for an actionable artificial intelligence bot based on data security correlations. An example method comprises determining, by a data platform implemented by a computing system, a plurality of tags for a snapshot executed by the data platform, detecting, by the data platform, an indication of a security breach relating to the snapshot, processing, by the data platform and using a machine learning model, a plurality of attributes of the security breach and the plurality of tags to identify a potential compromise of the snapshot, processing, by the data platform and using a large language model, at least the plurality of attributes to generate an actionable prompt including a natural language description of at least one security response, and outputting, by the data platform, the actionable prompt.

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 discloses a code generation method and device based on a large model, equipment, a medium and a product, and the method comprises the steps: carrying out the code generation through a first code large model according to target code demand data, and obtaining an initial code; according to the first code snippet, generating a test code through a second code large model to obtain a unit test code; according to the unit test code and the initial code, code repair based on editing distance regularization is conducted through a third code large model, and a to-be-analyzed code is obtained; performing iterative optimization on the to-be-analyzed code and the unit test code to obtain a target code corresponding to the target code demand data; according to the method, code generation, unit test generation and code repair are carried out in a multi-model cooperation mode, the reliability and accuracy of code generation can be effectively improved, software security vulnerabilities are reduced, and therefore the stability and security of a software system are improved.

The invention relates to the technical field of network security, and discloses a security vulnerability AI positioning method and system based on log files. The method comprises the following steps: firstly, analyzing user feedback describing open source component security vulnerabilities to extract fault key entity information, screening related log entries based on the fault key entity information, and constructing a vulnerability propagation graph; identifying a vulnerability triggering initial point set by using a graph traversal algorithm and a graph neural network, calling a component vulnerability case library to deduce a vulnerability utilization propagation chain and calculating a matching degree with a known open source component vulnerability utilization chain feature, and obtaining a maximum probability vulnerability utilization propagation chain and an associated vulnerability triggering initial point set; and generating a root cause positioning report, and updating the knowledge base or model training data according to a user verification result. According to the method, the initial vulnerability point and the complete vulnerability propagation chain of the security vulnerability of the open source component can be accurately and efficiently positioned, and the positioning accuracy and timeliness are improved.

The invention relates to a multi-protocol integration method and device, equipment, a storage medium and a program product, which are applied to a bastion host, and the bastion host comprises a communication port. The method comprises the following steps: firstly, receiving a first access request generated based on a first protocol and sent by each client through a communication port, then, determining target equipment needing to be accessed by each client according to the first access request, then, determining a second protocol supported by each target equipment based on preset configuration of each target equipment, and finally, sending the second protocol to the client through the communication port. And converting each first access request based on the first protocol and the second protocol, determining a second access request of each client, and accessing the corresponding target device through the second access request. By adopting the method, the number of communication ports can be reduced, so that the network attack surface is reduced, potential security holes and threats are reduced, and the access security of the target equipment is improved.

The invention discloses a dynamic access control method and device of a baseboard management controller, equipment and a storage medium, and relates to the technical field of server hardware security management.The method comprises the steps that when an access request is detected in a trusted operation environment, multi-dimensional context attributes associated with the access request are collected; performing dynamic trust evaluation based on the multi-dimensional context attribute, and generating a dynamic trust score corresponding to the access request; and executing an access control decision corresponding to the access request according to the dynamic trust score. Compared with a traditional static trust model, the dynamic trust evaluation is carried out based on the multi-dimensional context attribute under the trusted operation environment of starting verification of the baseboard management controller, and the differentiated access control decision is executed according to the dynamic trust score, so that the access risk is accurately identified, and the reliability of the system is improved. The security vulnerability of'permanent trust in one-time authentication 'in the prior art is avoided, and the access security of the substrate management controller is improved.

Disclosed is a method of more effectively managing and displaying an Information Security Management System (ISMS), or Cybersecurity Framework, by an application executing on a computer device for computing and displaying real time dynamic metrics and market comparison for the User. The method includes authenticated and authorized Users to conduct security baselines based on industry accepted standards in order to establish a plurality of metric baselines dynamically and in real time. The method further includes projects submitted to be measured against organizational goals and simulate the impact to the Security baselines. The method further includes the ability to provide financial visibility into the financial exposure of a Security Breach, or Data exfiltration and other available financial metrics. The method further includes a platform by which companies may request Virtual CISO's services from a pool of executive level resources.

The present disclosure provides techniques for red teaming with artificial intelligence (AI) models. A processing device generates, via a first AI model, an agent action space based on security data, where the agent action space is indicative of actions to perform to potentially compromise at least one of a computing system, a network, or an application. The processing device performs a reinforcement learning process with an agent based on the agent action space to obtain a log of the reinforcement learning process. The processing device generates, via a second AI model, a report based on the security data and at least a portion of the log, where the report is indicative of a security weakness of the at least one of the computing system, the network, or the application.

Described herein are systems and methods for discovering and proactively mitigating previously unknown security vulnerabilities. The systems and methods herein can utilize security vulnerability information to discover potential security threats and can utilize this information to generate an attack using a machine learning model, such as a large language model. Generated attacks can be carried out to assess impact of a security vulnerability. An output can be provided that represents the assessed impact. In some implementations, the systems and methods herein generate patches or other mitigations for security vulnerabilities, which can be tested and deployed to address security vulnerabilities.

The invention discloses a secure collaborative unloading method in an ultra-dense millimeter wave cache network, and the method specifically comprises the steps: constructing a network architecture, and configuring an optimization problem under multiple constraints in the network architecture; randomly generating an initial solution set according to the optimization problem, defining the initial solution set as an original population, and searching and iterating the original population by adopting an improved sparrow algorithm to obtain a target population; further searching by adopting an improved sparrow algorithm according to position information codes of all individuals in the target population, and outputting historical optimal position information; and performing safe collaborative unloading resource configuration according to the historical optimal position information of the target population. According to the method, the cache configuration is unloaded by the base station, and the task unloading cache configuration is jointly optimized and calculated under the conditions that the security vulnerability cost, the time delay limit, the user calculation capacity and the transmitting power are met, so that the task unloading energy consumption is reduced to the minimum.

The present disclosure of the various embodiments relates to using a large language model to assistant with the creation of secure code and / or the completion of threat modeling tasks in software development. In one example, a system comprises a computing device configure to identify a prompt that requests generating secure source code for source code with a security vulnerability. A security data source is queried for a security threat embedding. The security threat embedding is received from the security data source and an augmented prompt is generated. The augmented prompt is transmitted to the large language model. A secure source code is received from the large language model and imported into application source code in a software development environment.

The invention relates to the technical field of wireless network testing, and discloses a wireless router complete machine one-stop testing method and system and electronic device.The wireless router complete machine one-stop testing method comprises the steps that a typical network application characteristic flow mode library is constructed, and different flow modes are dynamically combined through the Markov decision process; creating a novel attack mode by using the generative adversarial network; constructing a multi-agent cooperative attack system; dynamically adjusting the test intensity according to the real-time response of the router; constructing a multi-dimensional evaluation index system and analyzing a test result; according to the invention, by combining artificial intelligence and network security technologies, the performance and security protection capability of the wireless router in a complex and changeable network environment and attack scene are comprehensively tested, potential stability problems and security holes are found in advance, and the product quality and the user experience are effectively improved.

The invention discloses a security vulnerability detection system for computer application software, and relates to the technical field of software security, the system comprises the following components: a multi-source data acquisition module for acquiring a security vulnerability by analyzing source codes, analyzing dependent files, capturing network resources and investigating a business process mode; a software code structure, third-party component dependence, a CVE vulnerability library, an attack chain knowledge library, industry compliance requirements and business process rule data are respectively collected, and standardized preprocessing is carried out; according to the invention, industry scenarized knowledge is fused through a knowledge graph to form a system containing association of "vulnerability hazard-business influence-compliance risk", and in a vulnerability detection and analysis module, based on association information output by a graph neural network, in combination with a node association relationship in the knowledge graph, the vulnerability hazard-business influence-compliance risk is determined. The method can accurately analyze the influence of vulnerabilities on the business process and the compliance risk caused by the vulnerabilities, and can generate a detailed vulnerability detection report.

The present disclosure provides a method for analyzing exploitability of memory safety vulnerabilities in binary programs. The method includes identifying potential vulnerabilities within a binary program, performing a baseline analysis to detect potential Return-Oriented Programming (ROP) chains, applying a memory safety mitigation technology to the binary program, performing a protected analysis after applying the memory safety mitigation technology to detect potential ROP chains, comparing results of the baseline analysis and the protected analysis, and generating a report quantifying an impact of the memory safety mitigation technology on exploitability of the identified vulnerabilities. The method enables assessment of the effectiveness of memory safety mitigation techniques in reducing the risk of exploitation, providing valuable insights for improving software security throughout the development lifecycle.

Systems and methods are provided for proactive dependency management in software development projects, including initiating a dependency scan within a version-controlled repository to identify and list project dependencies at predetermined intervals, and conducting a health analysis for the project dependencies listed by accessing and utilizing data from a plurality of vulnerabilities databases, the analysis uncovering current security vulnerabilities and assessing a frequency and recency of maintenance updates. A multifaceted criteria matrix is applied to analyzed dependencies to isolate those that exhibit indicators of potential risk, including known security vulnerabilities and evidence of neglect of updates and maintenance. A set of alternative dependencies is aggregated for identified at-risk dependencies by leveraging public repository analysis to discern commonly adopted replacements, further evaluating the alternative dependencies for compatibility with a technology stack of the software development projects and adherence to security and maintenance standards.

The invention discloses an intelligent detection method for network security vulnerabilities, and relates to the technical field of network security vulnerability detection, and the method comprises the following steps: analyzing source codes to construct an abstract syntax tree, a control flow graph and a call graph, identifying sensitive operation points based on a rule base, and screening hotspot functions in combination with code indexes and the call graph; extracting a candidate path from a program entry to a sensitive operation point, encoding to generate a path context snapshot, and scoring through a pre-training model; selecting a high-risk path to execute sparse symbolic execution, simplifying constraints by using variable interval information, and dynamically feeding back and adjusting scores; poC input verification is generated for the path triggering the vulnerability constraint, a report containing the path constraint, the triggering condition and the PoC is output, program analysis and machine learning are combined, the vulnerability detection efficiency and precision are improved, and the method is suitable for code security analysis of scenes such as an embedded system.

Embodiments of the present invention provide a system for identifying security vulnerabilities in software code. The system is configured for extracting, from an entity system, internal standards associated with software code of entity applications associated with an entity, extracting external standards associated with the software code of the entity applications from external systems, extracting severity ratings associated with known vulnerabilities, calculating modified severity ratings associated with the known vulnerabilities that are specific to the entity, performing assessment of the software code associated with the entity applications, via an artificial intelligence engine, to generate an output associated with the assessment of the software code based at least on the internal standards, the external standards, and the modified severity ratings, and performing one or more actions based on the generated output associated with the assessment of the software code.

The invention relates to the technical field of information security, in particular to a computer intelligent information security processing system, which comprises a track generation module for acquiring initial parameters of a user as seed input for initializing a chaotic mapping function and generating a unique chaotic track; the dynamic structure module is used for dynamically constructing a nonlinear encryption structure function family according to the chaotic trajectory so as to generate target data; and the salt value injection module is used for carrying out content-aware partitioning on the target data and injecting a dynamic salt value generated based on a timestamp, a chaos value and a context. During use, by dynamically generating nonlinear encryption logic, security vulnerabilities caused by a fixed formula structure are avoided; extraction codes with high safety and unpredictability are extracted from the data subjected to salt value injection processing, high safety and time sensitivity of the extraction codes on the local data level are achieved, and the unpredictability of visual data protection is enhanced based on pseudo-random processing of the actual content of the image.

The real-time ransom and security breach detection and prevention system empowers users and system administrators by providing real-time threat detection and prevention by detecting any phishing links, malicious executables and objects, and manipulation of authentication tokens that could potentially escalate a threat into ransom attacks, security breaches or other cyber attacks, and take immediate action by blocking and reporting the potential security breach before escalating into a real attack. The real-time ransom and security breach detection and prevention system generates a first hash of the link, executable, object, or authentication token. Utilizing this generated first hash, the real-time ransom and security breach detection and prevention system calculates a second hash upon user interaction or at runtime and compares the two unique hashes to determine if the link, executable, object, or authentication token is corrupt and terminates the gateway to prevent the cyber-threat from infecting the system.

The invention discloses a user dynamic access control method and system based on multi-dimensional data, and belongs to the technical field of software. The invention aims to solve the problems of user access control policy jitter, security vulnerability and difficulty in explaining compliance caused by static threshold, module independent decision and non-explicit modeling user experience cost and compliance risk cost in the prior art. Therefore, the invention provides a dynamic access control method, which comprises the following steps of: constructing a session-level multi-dimensional access context; dividing risk signal detection according to a visual angle; carrying out risk evidence combination and mode recognition; performing risk trajectory and strategy hysteresis control based on a state machine; unifying action arrangement and conflict resolution of the strategy center; strategy execution and session consistency control are carried out; and effect evaluation and strategy evolution based on multi-dimensional indexes are carried out. Compared with the prior art, multi-view evidence combination, session-level trajectory analysis, state machine and constraint solution and other means are introduced, multi-layer integration from a data layer, a logic layer, a time layer to a control layer is achieved, strategy jitter is effectively relieved, user experience is improved, service continuity is guaranteed, and compliance interpretability is enhanced.

A method includes: receiving an integrated circuit design; classifying, by the processing device, a signal path of a sub-circuit of the integrated circuit design based on a connection between an input port of the signal path and a component of the sub-circuit to generate a classification of the signal path; computing, by the processing device, a security vulnerability result of the sub-circuit of the integrated circuit design based on the classification of the signal path and based on a trust level of a zone in a fan-in cone to an input port of the signal path; and generating a security vulnerability report based on the security vulnerability result of the sub-circuit of the integrated circuit design.

The disclosure for a method for providing information. The traditional methods of managing information within vehicles, such as physical document storage and manual verification processes, are plagued by security vulnerabilities, limited accessibility, and organizational challenges. Stand-alone digital storage devices and cloud-based solutions offer alternatives but come with their own set of concerns, including security risks and dependence on internet connectivity. To address these shortcomings, the present disclosure proposes a comprehensive solution for securely managing and accessing information within the vehicle environment. By leveraging electronic devices within vehicles, encryption techniques, user authentication methods, and dynamic document management, the present disclosure ensures both security and convenience. This integrated approach enhances user experience, facilitates emergency response, and minimizes the risk of document loss or damage. The present disclosure represents a significant advancement in information management within vehicles, offering practical solutions to longstanding challenges.

The invention discloses an API security vulnerability dynamic monitoring method based on exception monitoring, and belongs to the technical field of computer security protection, and the method comprises the steps: obtaining request layer data and response layer data of a target API, and generating a flow data set; extracting behavior characteristics from the traffic data set, and generating a behavior characteristic matrix; performing context sensing analysis on the behavior feature matrix, and generating a vulnerability chain prediction result in combination with a business object mapping relationship; analyzing vulnerability path nodes in the vulnerability chain prediction result, and generating a temporary detection rule; and detecting the subsequent API request in real time by using the temporary detection rule, and outputting a security detection result. According to the method, traffic feature extraction, vulnerability chain prediction and adaptive rule generation mechanisms are adopted, and service context-associated real-time threat interception and continuously optimized security protection closed loop can be realized.

The invention discloses a cloud password service-oriented fuzzy test and compliance evaluation platform and method, and relates to the technical field of cloud computing and password security evaluation. The evaluation platform comprises a demand analysis module, a test case generation module, a fuzzy test execution module, a compliance detection module, a result fusion analysis module and a report generation module, and the result fusion analysis module is connected with the fuzzy test execution module and the compliance detection module. And the cloud password service detection module is used for carrying out association analysis on the response data, the abnormal log and the compliance detection result acquired by the fuzzy test, identifying the security vulnerability type and the compliance defect level of the cloud password service, and removing a repeated detection result. Fuzzy testing and compliance detection are organically fused, correlation analysis of the two results is achieved through the result fusion analysis module, the problems that in the prior art, the two tests are separated, and the results are difficult to integrate are solved, and the safety compliance condition of the cloud password service can be comprehensively reflected.

The invention discloses a network security vulnerability test training scene generation system and method, and relates to the field of network security and training teaching, and the system comprises a dynamic environment generation module, a flow collection and behavior perception module, an AI intelligent evaluation engine, an intelligent tutoring module, and a central control and management platform. Dynamic evolution of a practical training environment is realized through a knowledge graph and reinforcement learning, a complete behavior sequence is constructed through multi-source data fusion, an AI engine evaluates student operation from multi-dimensional fine granularity, an intelligent tutoring module provides real-time prompt, a redisk report and a personalized learning path, and a central platform realizes global management and control. The problems that an existing system is static, evaluation is one-sided and personalized tutoring is lacked are solved, the practical training combat performance and the teaching efficiency are improved, and the labor cost is reduced.

The invention discloses an intelligent contract security vulnerability detection method and system based on a large language model, and relates to the technical field of information security. Configuring training parameters of the large-scale language model, uploading a training data set and training the large-scale language model; carrying out additional reinforcement training on the trained large language model; configuring a chat client through a large-scale language model tool, setting a prompt project, inputting the prompt project into the chat client, and further optimizing the large-scale language model after reinforcement training by using a reinforcement learning method based on the prompt project to obtain a final optimized model; verifying the optimized final model by using a verification data set; and performing security vulnerability detection on the smart contract by taking the verified final model as a security vulnerability detection model. The problems that in the prior art, complex logic vulnerabilities are difficult to detect, all possible execution paths cannot be covered, and resource consumption is high are solved.

To grasp appropriate operation procedures of a Web site including operation with sequentiality and to efficiently / effectively perform automatic patrol.SOLUTION: A security test system 1 examining whether or not there is security vulnerability in a Web application acquires and analyzes a Web page to be patrolled in an object Web site 3, sets to prompt information related to a content of the acquired Web page, and inputs it to a LLM 4, creates operation procedure information on the Web page, simulates operation regarding the Web page according to the operation procedure information, acquires an URL related to a link in the Web page, and registers it to a page list 14 to be patrolled.SELECTED DRAWING: Figure 1

According to examples, an apparatus includes a processor that may obtain and parse a pipeline code to determine how variables of the pipeline code relate to each other, and replace the variables in the parsed pipeline code with values to which the variables respectively represent, in which the values correspond to pipeline run sources and pipeline run targets of API calls. The processor may also identify how the pipeline run targets interact with the pipeline run sources of the API calls and build a dependency graph that maps the pipeline run sources with the pipeline run targets. Runtime resources may thus be mapped to source code in a pipeline run to provide visibility into actions carried out by the pipeline. This visibility may be used to determine whether there are security vulnerabilities in the pipeline run sources and / or targets such that the vulnerabilities may be addressed / overcome.