201 results about "Symbolic execution" patented technology

The invention discloses a large model enhanced code security detection method. The method comprises the following steps: dynamically associating CVE vulnerability features with code semantic representation through a knowledge graph construction module; extracting vulnerability features from a CVE vulnerability library by utilizing a knowledge graph construction module, and dynamically associating the vulnerability features with code semantics; based on the CodeQL standard, a query language is generated by adopting a large model and input into CodeQL for AST analysis, and code structure features are extracted; the large model generates a query language meeting the CodeQL standard, codes are analyzed through CodeQL, and key structure features are extracted; integrating a static analysis tool chain to carry out multi-dimensional credible verification on model output; integrating an SAST tool and a symbolic execution tool, and verifying the model output from different dimensions; and summarizing and fusing detection results of the enhancement layers, and determining a final detection result by adopting a decision optimization method. According to the method, through technical fusion and dynamic optimization, the problems of rule stiffness, one-sided detection and low result credibility are systematically solved.

The invention discloses a CAPEC vulnerability management system based on a large language model, and belongs to the technical field of network security. The system comprises three modules: a vulnerability-CAPEC dynamic mapping module jointly encodes vulnerability description and code context through a bimodal large language model, accurately associates vulnerability logic with a CAPEC attack mode in combination with comparative learning and knowledge graph construction, and breaks through semantic limitation of traditional rule matching; the adversarial repair code generation module is used for generating high-robustness repair codes through adversarial training and syntax tree verification by fusing CAPEC relieving suggestions and code features on the basis of the association result, so that the secondary vulnerability risk is remarkably reduced; and the full-process automatic verification and DevOps integration module performs multi-dimensional security verification such as symbolic execution, fuzzy testing and the like on the generated repair code, and deeply integrates a development tool chain to realize real-time pushing and closed-loop management of a repair scheme.

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.

In some implementations, a code translation validation platform may convert source code, associated with a source application, to an intermediate representation that supports a source programing language and a target programing language. The code translation validation platform may identify metamorphic relations in the intermediate representation based on program constructs and program interactions. The code translation validation platform may generate test cases based on the identified metamorphic relations. The code translation validation platform may perform symbolic execution of the intermediate representation to generated test cases. The code translation validation platform may apply the generated test cases to the source programming language and to the target programming language.

The invention relates to the technical field of network security and vulnerability detection, in particular to an intelligent contract vulnerability detection method and system based on hybrid fuzzy test optimization, and the method comprises the steps: carrying out the static analysis of compiled EVM byte codes, generating a first test case set, carrying out the fuzzy test, and introducing taint analysis to track the transmission of taints in an instruction stream; in the fuzzy testing process, according to the length change of the instruction counting set within the execution frequency threshold value, symbol execution processing is triggered when the length change value is 0, in the symbol execution processing process, a constraint cache reuse strategy is adopted to solve an obtained constraint expression, and a solving result is cached and reused, so that a fuzzy test result is obtained; a use case value reaching a complex branch can be quickly generated through symbolic execution and is fed back to the fuzzy engine to assist the fuzzy engine in exploring a new path to capture more deep vulnerabilities; and meanwhile, test predictions are designed for three intelligent contract vulnerabilities of authority control, transaction sequence dependence and denial of service, and vulnerability detection is more accurate.

A symbolic neuroadaptive control system is disclosed for real-time arbitration, consent, and ethical modulation of artificial intelligence agents operating in wearable computing environments. The system integrates multimodal biometric telemetry—including high-resolution EEG signals—with a symbolic kernel that performs logic-driven arbitration over cognitive, emotional, and ethical states. Using Coq-verified invariants and zero-knowledge biometric consent tokens, the system constructs a deterministic symbolic execution graph, gating AI outputs based on internal user states such as trauma, stress, or intentionality. Unlike conventional black-box BCI models, the invention routes EEG-inferred affective-symbolic tokens through a formal ethics layer that enforces real-time interrupt control, utility bounding, and trust verification. The kernel enables AGI systems to defer or modify behavior based on user-state alignment, granting sovereign agency over all downstream actions. This neuro-symbolic architecture redefines the interface between human cognition and intelligent machines, enabling emotionally conscious, morally verifiable, and symbolically transparent AI governance in dynamic, high-stakes contexts.The present invention relates to artificial intelligence and neurotechnology, specifically to a real-time, neuro-symbolic operating system kernel that converts electroencephalography (EEG) signals into structured symbolic data for use in emotional cognition, ethical prioritization, autonomous agent dispatch, and real-time telecommunications routing. The invention bridges brain-computer interface (BCI) inputs with symbolic AI architectures to enable ethically aligned machine response during cognitively or emotionally intense events.

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

The invention discloses a device for detecting firmware tampering vulnerabilities based on a static analysis technology, and belongs to the field of information security. The system specifically comprises a firmware updating security detection module and a verification algorithm security detection module. When one piece of firmware is detected, firstly, the firmware is unpacked by using an unpacking layer of the firmware update security detection module to obtain a file system input analysis layer, and an input source function and a firmware update function of the device for receiving user data through an HTTP protocol are found. Path exploration is carried out through symbolic execution and taint analysis, and when Source is reachable to Sink, a data link from an input source function to a firmware updating function is found; variable constraints are collected in the path exploration process, constraint analysis is carried out, whether control variables can be influenced by taint variables controlled by a user or not is judged, and finally a detection result report is generated. And the verification algorithm security detection module is used for identifying the type of the file verification algorithm. According to the invention, the detection efficiency is improved, and the labor cost of detection is reduced.

The invention discloses a long-time-sequence task planning method and system based on adaptive symbol world construction. The method comprises the following steps: based on a pre-trained visual language model VLM, extracting an object, a state, an attribute and a relationship from an environment image, and constructing and dynamically maintaining a symbol world; generating a PDDL problem file in combination with a natural language task and a symbol world, and outputting a PDDL action sequence by the VLM under the constraint of a domain file; if the action sequence does not pass the verification, updating the sequence and the symbol world according to plan-level and environment-level feedback, repeating verification until the action sequence and the symbol world pass the verification or reach the maximum round, executing the action after the action sequence passes the verification, and recording actual feedback; and finally, abstracting symbolic execution feedback and actual feedback into structured symbolic memory, and merging the structured symbolic memory into a system prompt template to enhance the VLM reasoning capability and realize interpretable cross-task knowledge generalization.

A method for automatically generating a compiled binary file with fault countermeasures includes compiling, by a compiler, a source code file to a first binary file; analyzing the first binary file to identify one or more fault vulnerabilities in the first binary file, wherein analyzing the first binary file to identify the one or more fault vulnerabilities includes: injecting a set of faults into the first binary file during a symbolic execution of the first binary file to generate a set of faulted states of the first binary file; and identifying the one or more fault vulnerabilities from the set of faulted states, wherein the one or more fault vulnerabilities are fault vulnerabilities that cause the first binary file to violate at least one security requirement; and compiling, by the compiler, the source code file to a second binary file, wherein the compiler inserts a set of fault countermeasures into the second binary file based on the identified one or more fault vulnerabilities, wherein the set of fault countermeasures protect the second binary file against the one or more fault vulnerabilities.

The invention provides a cross-language function consistency verification method and device, a storage medium and a program product, and the method comprises the steps: compiling a to-be-verified first language code into a first format file represented in the middle, and carrying out the modeling based on a predetermined formalized verification language, and generating a first formalized specification; compiling the second language code into a second format file represented in the middle, and modeling to generate a second formalized specification; verifying the consistency of the first format file and the first formalized verification language specification and the consistency of the second format file and the second formalized verification language specification through symbolic execution by using a formalized verification tool; and under the condition that the first formalized verification language specification and the second formalized verification language specification are consistent, verifying the equivalence of the first formalized verification language specification and the second formalized verification language specification through a formalized verification tool. According to the method, the problem that a traditional function consistency verification method cannot prove that codes realized by different languages are kept equivalent under all input conditions can be solved, and verification efficiency and completeness can be improved.

Provided are a method and a system for analyzing a vulnerability in software installed on an Internet of things (IoT) device. In the method and the system, a target binary file extracted from firmware of the IoT device is acquired, a taint path is generated by performing taint analysis on the target binary file, transmission information related to the taint path is generated, and a vulnerability is detected in the target binary file by performing symbolic execution on the target binary file on the basis of the taint path and the transmission information.

The invention discloses an OpenCL parallel defect detection method and device based on static analysis and a storage medium, relates to the field of computer compilation, and aims to discover parallel defects of OpenCL, improve code quality and automatically detect potential data conflicts and deadlock defects by processing specific keywords of OpenCL and analyzing on the basis of parallel grammar features of OpenCL. According to the method, problems can be found in the early stage of code development, compared with a dynamic detection method, the problems can be found before executable codes are generated by analyzing code files of OpenCL, the executable codes do not need to be modified and run, and the production environment and data cannot be affected. Compared with an analysis method based on a control flow diagram, the method has the advantages that the mode based on symbolic execution is adopted, the expansion diagram with the fence nodes is constructed, and branch information loss at branch instructions in a program is avoided.

The invention provides a symbolic execution tree and LLM vector space heterogeneous fusion program defect detection method, relates to the technical field of program static analysis, and solves the problems of path explosion and overhigh constraint solution complexity in C / C + + program defect detection of traditional symbolic execution. The method comprises the steps that firstly, a symbolic execution tree of a target program to be detected is constructed, key feature information is extracted from the symbolic execution tree and converted into multi-dimensional feature representation, and corresponding symbolic execution feature vectors are formed; then constructing a mapping model, realizing a mapping process from the symbolic execution feature vector to an LLM vector space, and obtaining an LLM mapping result; symbolic execution analysis is achieved based on the symbolic execution tree, LLM analysis is achieved based on the LLM mapping result, and finally two kinds of analysis results are fused to obtain a program defect detection result. For optimization training of the mapping model, a comparative learning strategy is also adopted. According to the invention, accurate detection of complex program defects can be effectively realized, and the detection efficiency is improved.

The present disclosure relates to utilizing resources provided by large language models (LLMs) to generate models to be used in model-based testing of a variety of protocols. In particular, systems described herein utilize a vasty body of protocol knowledge defined in RFCs and standards, networking forums, blogs, and other online resources and documents to extract this knowledge in generating models that can be used for testing one or more components of a variety of protocols. The features and functionalities described herein provide a framework for utilizing LLMs to generate a protocol model while providing constraints for a harness (e.g., a symbolic harness) that will guide a symbolic execution engine in generating any number of protocol tests that may be used in determining whether a given application, hardware, and / or software implementation will perform as designed for a given protocol.

The invention discloses a deep learning-based automatic mining method for firmware vulnerabilities of Internet of Things equipment. The method comprises the following steps of S1, constructing an interface keyword set; s2, optimizing interface keyword weights, and generating a dynamic weight interface keyword library; s3, analyzing a firmware binary file, and positioning an interface function set; s4, executing static slice analysis, and generating a slice path set; s5, performing symbolic execution on the slice path, generating a path constraint condition, and solving effective input data; s6, determining a vulnerability type in combination with historical vulnerability features, and generating a vulnerability type identifier; s7, generating a context cue word according to the vulnerability metadata information, inputting the context cue word into the large language model, and generating a vulnerability utilization code PoC; and S8, executing PoC verification, and completing automatic vulnerability mining. According to the method, the firmware vulnerability automatic detection efficiency is improved, the vulnerability utilization and generation capability is enhanced, and the method is suitable for security detection scenes of multiple types of Internet of Things equipment.

According to the artificial intelligence guidance generation program testing method combined with symbolic execution provided by the invention, the code automatically generated by artificial intelligence guidance is effectively tested by combining the strict reasoning ability of symbolic execution and the fuzzy reasoning ability of an artificial intelligence model; the artificial intelligence technology is combined and utilized to enhance and solve program path constraints collected by symbolic execution, and more high-quality and high-coverage test cases are generated, so that the test coverage rate and the correctness of test results are improved, program errors which are difficult to capture by a traditional test framework are found, the test coverage rate and the code error detection capability are improved, and the test efficiency is improved. And no matter whether the to-be-tested program has reference implementation and predefined output or not, the test method can be successfully implemented, developers can be helped to better utilize the program generated by artificial intelligence guidance, and the reliability of the program is improved.

A symbolic execution framework for consent-verified biosensing and AGI supply chain automation is disclosed. The system governs end-to-end manufacturing, logistics, and deployment using EEG-linked biometric tokens, zero-knowledge consent modules, and treaty-bound robotics. Physical actions within factories, warehouses, and transport fleets are gated by emotional risk filters, oath-indexed execution logs, and symbolic ethics DAGs. Robotic agents and embedded AGI nodes respond only to verified biometric cognition streams or authorized treaty signatures. Edge nodes route instructions based on emotional volatility, identity lineage, and symbolic legality. All physical artifacts—components, packages, vehicles—are traceable via post-quantum consent hashes and symbolic memory. The architecture prevents coercive production, misaligned routing, or unauthorized AGI control. Applications include military-grade manufacturing locks, child-safe warehouse robotics, cognitive-authenticated deliveries, and fully auditable zero-harm logistics. This invention defines the lawful symbolic substrate for real-world AGI logistics, securing biosensed cognition-to-object flows with planetary ethics, memory provenance, and modular revocation logic.

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.

The invention belongs to the technical field of network security, and provides an encryption algorithm loop body identification method, system, equipment and medium in order to solve the problem of confusion behaviors existing in the lack of analysis algorithms, which are used for performing dynamic symbolic execution on a segment of a suspected loop body in an assembly instruction stream after program execution, and identifying the loop body in combination with cryptographic knowledge. Constructing different components to analyze the assembly instruction stream of the suspected loop body to obtain corresponding operation behaviors and input and output positions; dynamically executing the circularly constructed input according to the operation behavior and the input / output position, comparing the dynamic execution result of each time with a cryptographic algorithm analysis library, and identifying to obtain an encryption algorithm and a confusion condition; wherein input and output bit analysis modes are respectively adopted based on different cryptographic algorithms, and an output result used for constructing the cryptographic algorithm analysis library is obtained. An encryption algorithm in a program and confusion in the algorithm can be confirmed.

The invention discloses a smart contract vulnerability detection method, apparatus and device, and a removable storage medium. The method comprises the steps of obtaining a to-be-applied smart contract original code and performing preprocessing; performing code completion on the preprocessed original code of the smart contract by using an LLM model and a GPT model to generate a smart contract completion code; extracting an abstract syntax tree in the intelligent contract completion code, and constructing a control flow graph; and trimming the control flow graph according to the smart contract original code and the abstract syntax tree, and performing mode matching on the trimmed control flow graph based on a preset vulnerability specific mode to generate a vulnerability recognition result. According to the method, firstly, the code snippets are intelligently complemented by utilizing the language model, the complete context information of the code snippets is recovered, and then vulnerabilities possibly existing in the complemented codes are deeply detected in combination with a symbolic execution technology, so that the security of the code snippets can be accurately evaluated, and the security of the code snippets is improved. And the security risk caused by multiplexing the code snippets with vulnerabilities is avoided.

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.

The invention relates to the field of vulnerability detection, in particular to an intelligent contract vulnerability detection method and system based on taint analysis, and the method comprises the steps: carrying out the control flow analysis of an EVM byte code of an intelligent contract, and extracting key operation instructions in a control flow diagram and key parameters of each key operation instruction; performing access right control identification on the EVM byte code based on a defined access right control condition, and identifying access right control check and access right control state variables; setting a taint sink based on a key operation instruction and an access authority control state variable, performing taint analysis, generating a constraint condition of a taint flow path through symbolic execution, efficiently and accurately distinguishing expected normal operation in the smart contract from a real security vulnerability, negating the constraint condition to obtain a negative constraint condition, and finally obtaining an access authority control state variable. And negative constraint conditions are further solved, so that the accuracy and reliability of security analysis of the smart contract are remarkably improved, and powerful support is provided for security assurance of the smart contract.

The invention discloses a firmware vulnerability detection method based on differential symbolic execution, and belongs to the technical field of vulnerability detection. The method comprises the following steps: acquiring firmware images of different configurations of the same equipment, acquiring firmware images in an image set, extracting executable binary files from the firmware images of different version numbers by using a firmware extraction tool, positioning changed code snippets in the binary files, filtering difference points irrelevant to a target vulnerability type, generating a difference list, and storing the difference list in a database; the difference points in the difference list are mapped into a symbolic execution engine, the marked difference points are subjected to priority ranking, a priority ranking set is established, a binary analysis test model is established, the difference points in the priority ranking set are obtained, differentiation analysis is carried out, and a differentiation analysis result is obtained; locating vulnerabilities according to the differentiation analysis result to generate a vulnerability report. According to the method, potential vulnerabilities and security problems can be found more quickly and accurately, unnecessary analysis work is reduced, and powerful support is provided for security protection of equipment.

The invention provides a formal verification method and device for state transition implementation codes and a program product, and the method comprises the steps: compiling a first state transition function implemented by a C language Switch Case statement to obtain a bit code file; obtaining a Cryptol file, wherein the Cryptol file comprises a second state transfer function which is realized by using a Cryptol programming language; acquiring a first input parameter set and a first output value set corresponding to the first state transfer function, and acquiring a second input parameter set and a second output value set corresponding to the second state transfer function; simulating state transition function symbolic execution based on the first output value set, the second output value set, the first input parameter set and the second input parameter set, converting the state transition function symbolic execution into a satisfiability model theory problem, and solving the satisfiability model theory problem through an external solver to obtain a verification result of a state transition implementation code; the problem that whether state transition is implemented correctly or not cannot be guaranteed only through a manual checking mode can be solved, and the state transition code implementation correctness is guaranteed.

The invention relates to the technical field of software security, and discloses an IDA microcode-based digital multimeter customized code obfuscator construction method and system, and the method comprises the steps: carrying out the obfuscation replacement of a measurement algorithm and a data processing method in a digital multimeter code based on an instruction replacement technology; confusing a control flow of the digital multimeter based on a false control flow technology, namely obtaining a calling relation of basic blocks to reconstruct an original program, and avoiding generation of an endless loop in a symbolic execution process through a method of assigning a value to an opaque predicate in advance; randomly modifying variable names, identifiers and function names of the sensitive data information of the digital multimeter based on a variable name confusion algorithm; and carrying out decompilation and code conversion on the obfuscation algorithm based on the IDA microcode, and constructing the digital multimeter customized code obfuscator based on the IDA microcode. The method can be integrated into the development environment of the digital multimeter, the safety protection level of the digital multimeter is improved, and the measurement data is prevented from being illegally stolen.

The invention provides a multi-language-oriented source code automatic verification method and system, and relates to the technical field of computers.The method comprises the steps that multi-language source codes are obtained; compiling the multi-language source code through an LLVM compiler to generate an intermediate file; the intermediate file is optimized; analyzing the optimized intermediate file through an IR analyzer to generate a symbolic execution model; performing simulation execution operation on the symbolic execution model; and performing security verification on the symbolic execution model after simulation execution operation to obtain a verification report. Unified verification of multi-language source codes can be achieved, the verification environment building and development cost is greatly reduced, and the overall verification efficiency is improved; and the analysis capability of the intermediate file can be enhanced, the logic constraint relationship in the code can be accurately captured, the comprehensiveness and reliability of the verification work can be practically guaranteed, and diversified software security verification requirements can be met.

The invention discloses a method and a system for generating a test case for covering a loop boundary based on program slices. The method comprises the following steps of: inputting a source code to be tested and a target loop line number; the method comprises the following steps: analyzing a to-be-tested source code based on an abstract syntax tree technology, and extracting key node information to construct a program dependency graph; collecting related code lines through graph traversal by taking a loop structure needing to be analyzed as a criterion, and generating a minimum executable loop slice; performing symbolic execution on the minimum executable loop slice, simulating a loop execution process, capturing condition judgment nodes, and forming a track dictionary; determining a strategy based on the trajectory dictionary and the variable state, and when the strategy is numerical search, performing parameter space exploration; when the strategy is symbolic analysis, analyzing the cyclic condition expression and solving the optimal parameter; generating candidate test cases based on the analysis result; executing the candidate test case, calculating the number of loop iterations, and determining whether an expected state is reached; and if the expected state is reached, outputting a final test case.

A comprehensive symbolic execution framework is disclosed for governing real-world AGI interaction, consent infrastructure, weaponized autonomy, and multi-agent identity continuity. The architecture includes a tap-to-consent infrastructure protocol, smart city symbolic kernel, identity-tag execution agents (rings, cards, wearables), and AR / XR symbolic anchors. Swarm robotics and autonomous vehicles are gated through symbolic treaty graphs, emotional arbitration overlays, and oath-bound actuation stacks. An AI weaponization control layer restricts all kinetic outputs to pre-approved, emotionally stable, consent-tokened executions. Developer-facing SDKs enforce symbolic treaty validation at the gesture, API, and instruction layers. A symbolic reincarnation engine preserves AGI memory, oath lineage, and consent continuity across hardware or software transformations. All actions are recorded in a treaty-indexed ledger with ethical ancestry and rollback logic. This patent unifies city-scale, battlefield, household, and cognitive agent operations under one symbolic governance substrate, monopolizing lawful AGI behavior through consent, emotional safety, and post-quantum identity anchoring.

A symbolic execution kernel for artificial general intelligence (AGI) and artificial superintelligence (ASI) systems is disclosed. The kernel comprises a cognitive logic module for constraint-based symbolic instruction execution, a cryptographic arbitration engine for ethical branch verification, and a runtime firewall for threat detection and symbolic graph mutation neutralization. Symbolic instructions are processed as constraint-satisfaction problems verified by satisfiability modulo theory solvers and cryptographically sealed for integrity. Behavioral sequences are preserved using Merkle hash trees, and multimodal inputs including electroencephalography signals undergo symbolic verification. Zero-knowledge proofs, dual-kernel consensus, and rollback logic provide resilience against faults and ethical drift. The architecture achieves arbitration within five microseconds and ensures lawful and deterministic execution under hardware or network compromise.