# code-gen-security
21 artikelengetagd met “code-gen-security”
AI gebruiken voor kwetsbaarheidsonderzoek
How to leverage AI coding assistants for vulnerability research, including automated code audit, fuzzer generation, exploit development, and responsible disclosure.
Methodologie voor het auditen van door AI gegenereerde code
Structured audit methodology for evaluating the security of AI-generated code, covering static analysis, dynamic testing, and organizational assessment.
Governance-frameworks voor AI-codegeneratie
Organizational governance frameworks for managing AI code generation risk, covering policy development, risk assessment, compliance, and maturity models.
Beveiligingsvergelijking van AI-codereviewtools
Security analysis and comparison of AI-powered code review tools, evaluating their vulnerability detection capabilities and inherent risks.
Beveiligingshiaten in AI-gegenereerde tests
Analyzing how AI-generated test suites systematically miss security-relevant test cases, creating dangerous coverage illusions.
Beveiligingsrisico's van AI-ondersteund refactoren
Analysis of security vulnerabilities introduced when AI tools refactor existing code, including subtle behavioral changes and security property violations.
Beveiligingsanalyse van de Aider-codeerassistent
Beveiligingsbeoordeling van de Aider AI-pairprogrammingtool, met behandeling van de git-integratie, modelrouting, repository-toegangspatronen en overwegingen rond de toeleveringsketen.
Beveiliging van AI-gegenereerde API-endpoints
Analysis of security vulnerabilities in AI-generated REST and GraphQL API code, covering authentication bypass, BOLA, mass assignment, and rate limiting failures.
Beveiligingsanalyse van de Claude Code CLI
In-depth security assessment of Claude Code CLI covering its permission model, tool execution, MCP integration, and enterprise security considerations.
Licentiecompliance in door AI gegenereerde code
Legal and compliance risks of AI-generated code including license contamination, copyright exposure, and organizational governance for code generation tools.
Promptextractie uit codegeneratietools
Techniques for extracting system prompts, custom instructions, and proprietary configurations from AI code generation tools.
Sandboxing van AI-codegeneratie
Design patterns for sandboxing AI code generation and execution, covering container isolation, capability restriction, network controls, and runtime monitoring.
Supply chain-risico's bij AI-codegeneratie
Analysis of supply chain attack vectors introduced by AI code generation tools, including dependency confusion, typosquatting, and training data poisoning.
Beveiligingsanalyse van Copilot Workspace
Security evaluation of GitHub Copilot Workspace, analyzing attack surfaces in AI-driven multi-file code generation and planning.
Beveiligingsanalyse van de Cursor AI-IDE
Comprehensive security assessment of Cursor AI IDE covering its architecture, data handling, extension model, and attack surfaces for AI-assisted development.
Beveiliging van door LLM gegenereerde Dockerfiles
Analyzing security vulnerabilities commonly introduced by AI-generated Dockerfiles and container configurations.
SQL-injectie via LLM-codegeneratie
How LLMs generate SQL injection vulnerabilities through string formatting, improper parameterization, and ORM misuse, with detection and prevention strategies.
XSS-kwetsbaarheden door AI-gegenereerde code
Analysis of cross-site scripting patterns produced by LLM code generation, covering DOM XSS, reflected XSS, and framework-specific bypass patterns.
Beveiliging van multi-agent-codeersystemen
Security analysis of multi-agent AI coding systems covering inter-agent trust, privilege escalation, tool-use chains, and emergent behavior risks.
Beveiliging van AI-gegenereerde smart contracts
Security analysis of AI-generated Solidity smart contracts covering reentrancy, integer overflow, access control, and automated vulnerability detection.
Beveiliging van AI-gegenereerde infrastructure as code (Terraform)
Security risks in AI-generated Terraform configurations including privilege escalation, network exposure, secret management failures, and compliance violations.