{"data":{"id":"2d64d4bc-ab30-4410-870e-042f002fdbd0","title":"The LLM-Attacker: A Unified Red-Team Framework for Privacy Evaluation of Split and Distributed LLM Systems","summary":"LLM-Attacker is a red-team framework for evaluating privacy in split and distributed LLM systems, where input text stays on the client and intermediate representations go to a server. It records attacker access, datasets and scoring rules so that evaluations can be checked and compared, and it requires a linked undefended reference before a defence can pass. In a controlled GPT-2 experiment, returning gradients during training raised token recovery from 94.20% to 97.38% and exact-document recovery from 13.71% to 37.77%.","solution":"N/A -- no mitigation discussed in source.","labels":["security","privacy","research"],"sourceUrl":"https://arxiv.org/abs/2610.22722v1","publishedAt":"2026-10-15T22:53:45.000Z","cveId":null,"cweIds":null,"cvssScore":null,"cvssSeverity":null,"severity":"info","attackType":["data_extraction","pii_leakage"],"issueType":"research","affectedPackages":null,"affectedPackageNames":null,"affectedPackageRefs":null,"affectedVendors":[],"affectedVendorsRaw":["GPT-2"],"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":"2026-10-15T22:53:45.000Z","capecIds":null,"crossRefCount":0,"attackSophistication":"advanced","impactType":["confidentiality"],"aiComponentTargeted":"rag","llmSpecific":true,"classifierConfidence":0.9,"researchCategory":"preprint","atlasIds":null}}