{"data":{"id":"6b9cfaba-a896-4aba-a8e8-5b3ae69a054f","title":"PI-SAFE: Practical Privacy-Preserving LLM Inference With Adversarial Fine-Tuning for Optimized Utility","summary":"Cloud-based LLM services usually require users to send their text inputs unencrypted, which creates privacy risks. This paper presents PI-SAFE, a framework that protects privacy during LLM inference (the process of running a model to generate outputs) by splitting the model between client and server and sending obfuscated intermediate representations (partially hidden data states) instead of raw text, combined with adversarial fine-tuning (training the model with specially crafted examples to make it harder to attack) to prevent attackers from reconstructing the original input.","solution":"N/A -- no mitigation discussed in source.","labels":["security","privacy"],"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","membership_inference"],"issueType":"research","affectedPackages":null,"affectedVendors":[],"affectedVendorsRaw":[],"classifierModel":"claude-haiku-4-5-20251001","classifierPromptVersion":"v3","cvssVector":null,"attackVector":null,"attackComplexity":null,"privilegesRequired":null,"userInteraction":null,"exploitMaturity":null,"epssScore":null,"patchAvailable":null,"disclosureDate":"2026-09-01T13:17:12.000Z","capecIds":null,"crossRefCount":0,"attackSophistication":"advanced","impactType":["confidentiality"],"aiComponentTargeted":"inference","llmSpecific":true,"classifierConfidence":0.92,"researchCategory":"peer_reviewed","atlasIds":null}}