{"data":{"id":"3e67423f-7348-4660-af0d-6e9a33f89f16","title":"Black-Box Red-Teaming of Multi-Agent Systems via Reinforcement Learning","summary":"Researchers introduce ReMAS, a reinforcement learning red-teaming framework for multi-agent systems (MAS) built on LLM agents. The framework fine-tunes attacker LLMs in two steps: a rewrite stage that refines base attack prompts, then a template generation stage that targets specific sub-agents to extract their system prompts. The authors report substantially higher attack success rates than existing black-box attacks, with transferability across backend LLMs and MAS structures.","solution":"N/A -- no mitigation discussed in source.","labels":["security","research"],"sourceUrl":"https://arxiv.org/abs/2610.28966v1","publishedAt":"2026-10-20T22:32:22.000Z","cveId":null,"cweIds":null,"cvssScore":null,"cvssSeverity":null,"severity":"low","attackType":["prompt_injection","other"],"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":"2026-10-20T22:32:22.000Z","capecIds":null,"crossRefCount":0,"attackSophistication":"advanced","impactType":["confidentiality"],"aiComponentTargeted":"agent","llmSpecific":true,"classifierConfidence":0.95,"researchCategory":"preprint","atlasIds":["AML.T0051"]}}