{"data":{"id":"6b9cfaba-a896-4aba-a8e8-5b3ae69a054f","title":"PI-SAFE: Practical Privacy-Preserving LLM Inference With Adversarial Fine-Tuning for Optimized Utility","summary":"PI-SAFE is a framework for privacy-preserving LLM inference that splits a pre-trained model between client and server, so clients send obfuscated intermediate representations instead of raw text and the server-side model needs no changes. It adds Dual-constraint Adversarial Fine-Tuning (AdvFT) and a random prefix-based obfuscation (PI-SAFE+) to resist reconstruction attacks. The authors report that PI-SAFE+ cuts the success rate of optimization-based reconstruction attacks to near zero and drives attribute inference to random-guessing levels.","solution":"N/A -- no mitigation discussed in source.","labels":["security","privacy","research"],"sourceUrl":"http://ieeexplore.ieee.org/document/11673958","publishedAt":"2026-09-01T13:17:12.000Z","cveId":null,"cweIds":null,"cvssScore":null,"cvssSeverity":null,"severity":"info","attackType":["data_extraction"],"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":"2026-09-01T13:17:12.000Z","fixReleasedAt":null,"daysToFix":null,"fixCheckedAt":null,"capecIds":null,"crossRefCount":0,"attackSophistication":"advanced","impactType":["confidentiality"],"aiComponentTargeted":"inference","llmSpecific":true,"classifierConfidence":0.93,"researchCategory":"peer_reviewed","atlasIds":null}}