{"total":37,"rows":[{"id":"2410.14273","title":"REEF: Representation Encoding Fingerprints for Large Language Models","created":"2024-10-18","cited_by_count":36,"area":null},{"id":"2506.06975","title":"Auditing Black-Box LLM APIs with a Rank-Based Uniformity Test","created":"2025-06-08","cited_by_count":18,"area":null},{"id":"2502.00706","title":"Model Provenance Testing for Large Language Models","created":"2025-02-02","cited_by_count":15,"area":null},{"id":"2508.19843","title":"SoK: Large Language Model Copyright Auditing via Fingerprinting","created":"2025-08-27","cited_by_count":15,"area":null},{"id":"2509.26404","title":"SeedPrints: Fingerprints Can Even Tell Which Seed Your Large Language Model Was Trained From","created":"2025-09-30","cited_by_count":10,"area":null},{"id":"2509.26598","title":"Are Robust LLM Fingerprints Adversarially Robust?","created":"2025-09-30","cited_by_count":8,"area":null},{"id":"2511.14045","title":"Auditing Data Membership in Reinforcement Learning With Verifiable Rewards","created":"2025-11-18","cited_by_count":8,"area":null},{"id":"2603.01919","title":"Real Money, Fake Models: Deceptive Model Claims in Shadow APIs","created":"2026-03-02","cited_by_count":8,"area":null},{"id":"2411.05197","title":"Hardware and Software Platform Inference","created":"2024-11-07","cited_by_count":7,"area":null},{"id":"2607.10252","title":"One Token Is Enough: Fingerprinting and Verifying Large Language Models from Single-Token Output Distributions","created":"2026-07-11","cited_by_count":6,"area":null},{"id":"2501.18712","title":"Invisible Traces: Using Hybrid Fingerprinting to identify underlying LLMs in GenAI Apps","created":"2025-01-30","cited_by_count":5,"area":null},{"id":"2602.11083","title":"Token-Efficient Change Detection in LLM APIs","created":"2026-02-11","cited_by_count":5,"area":null},{"id":"2501.16029","title":"FDLLM: A Dedicated Detector for Black-Box LLMs Fingerprinting","created":"2025-01-27","cited_by_count":4,"area":null},{"id":"2504.01032","title":"Who Owns the Output? Bridging Law and Technology in LLMs Attribution","created":"2025-03-29","cited_by_count":4,"area":null},{"id":"2510.06738","title":"AWM: Accurate Weight-Matrix Fingerprint for Large Language Models","created":"2025-10-08","cited_by_count":3,"area":null},{"id":"2601.14311","title":"Tracing the Data Trail: A Survey of Data Provenance, Transparency and Traceability in LLMs","created":"2026-01-19","cited_by_count":3,"area":null},{"id":"2605.29524","title":"KBF: Knowledge Boundary as Fingerprint for Language Model and Black-Box API Auditing","created":"2026-05-28","cited_by_count":3,"area":null},{"id":"2410.04454","title":"Inner-Probe: Discovering Copyright-related Data Generation in LLM Architecture","created":"2024-10-06","cited_by_count":1,"area":null},{"id":"2508.09021","title":"Attacks and Defenses Against LLM Fingerprinting","created":"2025-08-12","cited_by_count":1,"area":null},{"id":"2601.11683","title":"Attesting Model Lineage by Consisted Knowledge Evolution with Fine-Tuning Trajectory","created":"2026-01-16","cited_by_count":1,"area":null},{"id":"2602.00772","title":"Provable Model Provenance Set for Large Language Models","created":"2026-01-31","cited_by_count":1,"area":null},{"id":"2605.15118","title":"Talk is (Not) Cheap: A Taxonomy and Benchmark Coverage Audit for LLM Attacks","created":"2026-05-14","cited_by_count":1,"area":null},{"id":"2605.29979","title":"Fingerprinting Inference Systems of Large Language Models","created":"2026-05-28","cited_by_count":1,"area":null},{"id":"2609.31877","title":"A Large-Scale Benchmark and Risk Assessment of Traffic Analysis Attacks on Cloud LLM Services","created":"2026-09-25","cited_by_count":1,"area":null},{"id":"2510.06525","title":"Text-to-Image Models Leave Identifiable Signatures: Implications for Leaderboard Security","created":"2025-10-07","cited_by_count":0,"area":null},{"id":"2601.09647","title":"Identifying Models Behind Text-to-Image Leaderboards","created":"2026-01-14","cited_by_count":0,"area":null},{"id":"2603.26328","title":"Verify Claimed Text-to-Image Models via Boundary-Aware Prompt Optimization","created":"2026-03-27","cited_by_count":0,"area":null},{"id":"2605.16035","title":"Who Owns This Agent? Tracing AI Agents Back to Their Owners","created":"2026-05-15","cited_by_count":0,"area":null},{"id":"2607.01313","title":"Black-Box Inference of LLM Architectural Properties with Restrictive API Access","created":"2026-07-01","cited_by_count":0,"area":null},{"id":"2607.25227","title":"Decision-Level Hijacking: Injecting Cognitive Bias into Large Language Models via Bit-Flip Attacks","created":"2026-07-28","cited_by_count":0,"area":null},{"id":"2607.25880","title":"Stemma: Induced Decision Regions Reveal LLM Provenance","created":"2026-07-28","cited_by_count":0,"area":null},{"id":"2608.07786","title":"Who Built This Model? Tracing LLM Lineage via Spectral Fingerprints in Weight Space","created":"2026-08-07","cited_by_count":0,"area":null},{"id":"2608.16391","title":"Ventor-QTest: Threat-Model-Driven Verification of Vendor-Hosted LLM APIs","created":"2026-08-17","cited_by_count":0,"area":null},{"id":"2608.27954","title":"Not to Break, but to Attest: Adversarial Probes for Privacy-Preserving LLM Verification","created":"2026-08-28","cited_by_count":0,"area":null},{"id":"2609.20614","title":"Inference-Engine Fingerprinting Attacks are Practical: Exploring Model-Driven Environmental Discovery, Exploitation, and Escape","created":"2026-09-17","cited_by_count":0,"area":null},{"id":"2609.38831","title":"Forging LLM Authorship Fingerprints with Targeted Rewriting","created":"2026-09-30","cited_by_count":0,"area":null},{"id":"2610.00253","title":"System Attribution in LLM Brand Recommendations: Single Responses Identify the System, Aggregated Brand Profiles Do Not Transfer","created":"2026-09-23","cited_by_count":0,"area":null}],"next_cursor":null}