{"data":{"id":"12367897-00e9-48b5-926e-641d9040157a","title":"Agentic and generative AI for intelligent autonomous vulnerability assessment and penetration testing: A systematic analysis","summary":"A PRISMA-guided systematic review of 72 studies from 2020 to 2025 analyses Agentic AI, reinforcement learning, LLMs and tool-augmented Generative AI for autonomous vulnerability assessment and penetration testing across IT and OT environments. It finds agentic approaches suit sequential tasks such as reconnaissance orchestration and lateral movement, while generative approaches excel at reasoning over unstructured evidence and producing traceable audit reports. The authors report that none of the coded studies was evaluated against a physical or emulated OT/IIoT target, and they name hallucination control and prompt injection as open challenges.","solution":"N/A -- no mitigation discussed in source.","labels":["research","security"],"sourceUrl":"https://doi.org/10.1016/j.cosrev.2026.101083","publishedAt":"2026-10-24T00:00:00.000Z","cveId":null,"cweIds":null,"cvssScore":null,"cvssSeverity":null,"severity":"info","attackType":[],"issueType":"research","affectedPackages":null,"affectedPackageNames":null,"affectedPackageRefs":null,"affectedVendors":[],"affectedVendorsRaw":[],"classifierModel":"claude-haiku-5-5","classifierPromptVersion":"v4","summaryPromptVersion":"v2","headline":null,"headlinePromptVersion":null,"cvssVector":null,"attackVector":null,"attackComplexity":null,"privilegesRequired":null,"userInteraction":null,"exploitMaturity":null,"epssScore":null,"epssCheckedAt":null,"kevDateAdded":null,"advisoryAliases":null,"affectedPackagesSource":null,"affectedPackagesCheckedAt":null,"patchAvailable":null,"disclosureDate":"2026-10-24T00:00:00.000Z","capecIds":null,"crossRefCount":0,"attackSophistication":"moderate","impactType":null,"aiComponentTargeted":null,"llmSpecific":false,"classifierConfidence":0.85,"researchCategory":"peer_reviewed","atlasIds":null}}