FLIPS: Instance-Fingerprinting for LLMs via Pseudo-random Sequences

• URL: http://arxiv.org/abs/2606.03330v1
• Date: Tue, 02 Jun 2026 08:39:50 GMT
• Title: FLIPS: Instance-Fingerprinting for LLMs via Pseudo-random Sequences
• Abstract summary: We introduce instance-level fingerprinting, a regulator-oriented paradigm that distinguishes configurations of the same Large Language Model.<n>Our method FLIPS, exploits biases in generated binary random sequences to reach 96% (closed-set) and 90% (open-set) identification accuracy across 237 model instances.<n>This shows that instance-level fingerprinting is both necessary for regulation and practically feasible.
• Score: 4.351505522514463
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Literature reveals that a Large Language Model's (LLM) behavior is not only conditioned by its original weights but also its instance-level parameters, such as instructional prompt, sampling configuration or quantization. A model that generates safe outputs under one configuration may produce toxic content under another. However, current LLM identification techniques (such as fingerprinting) focus on intellectual property protection, and their design favors robustness to changes in these instance-level parameters. This poses a critical challenge for AI regulation in which compliance assessments target actual deployed behaviors, not model provenance. In this paper, we introduce instance-level fingerprinting, a regulator-oriented paradigm that distinguishes configurations of the same LLM. Our method FLIPS, exploits biases in generated binary random sequences to reach 96% (closed-set) and 90% (open-set, where some targets are unknown) identification accuracy across 237 model instances, versus 35% for the adapted LLMmap baseline. This shows that instance-level fingerprinting is both necessary for regulation and practically feasible. Code available at https://github.com/GurvanR/FLIPS-LLM-Instance-Fingerprinting.

Related papers

• From Attack Simulation to SIEM Rule: Deterministic Detection-as-Code Synthesis with Probe-Level Traceability [51.56484100374058]
  Security teams simulate attacks against their own systems to check whether their monitoring would catch a real intruder.<n>A human bridges that gap by hand, reading each finding and writing the corresponding Sigma rule.<n>We show this translation can be partially automated when probes are drawn from a locked corpus.
  arXiv  Detail & Related papers  (2026-06-03T14:26:25Z)
• Prompt2Fingerprint: Plug-and-Play LLM Fingerprinting via Text-to-Weight Generation [46.76055970078189]
  We propose Prompt2Fingerprint, a framework that reformulates fingerprinting as a conditional parameter generation task.<n>By leveraging a specialized generator, P2F maps textual descriptions directly to low-rank parameter increments in a single forward pass.<n>Our experiments demonstrate that P2F maintains high fingerprint accuracy, harmlessness, and robustness while significantly reducing computational overhead.
  arXiv  Detail & Related papers  (2026-05-18T14:30:32Z)
• Alignment Imprint: Zero-Shot AI-Generated Text Detection via Provable Preference Discrepancy [51.887915969023965]
  We show that modern Large Language Models (LLMs) undergo alignment, leaving a measurable distributional imprint.<n>To mitigate the instability in high-entropy regions, we introduce Log-likelihood Alignment Preference Discrepancy (LAPD)<n>LAPD is a standardized information-weighted statistic based on alignment imprint.
  arXiv  Detail & Related papers  (2026-04-18T09:12:24Z)
• iSeal: Encrypted Fingerprinting for Reliable LLM Ownership Verification [22.052342142871144]
  iSeal is a fingerprinting method designed for reliable verification when the model thief controls the suspected LLM in an end-to-end manner.<n>It injects unique features into both the model and an external module, reinforced by an error-correction mechanism and a similarity-based verification strategy.<n>iSeal achieves 100 percent Fingerprint Success Rate on 12 LLMs against more than 10 attacks, while baselines fail under unlearning and response manipulations.
  arXiv  Detail & Related papers  (2025-11-12T02:30:19Z)
• SeedPrints: Fingerprints Can Even Tell Which Seed Your Large Language Model Was Trained From [65.75182441010327]
  We propose a stronger and more intrinsic notion of LLM fingerprinting: SeedPrints.<n>We show that untrained models exhibit reproducible token selection biases conditioned solely on their parameters.<n> Experiments on LLaMA-style and Qwen-style models show that SeedPrints achieves seed-level distinguishability and can provide birth-to-lifecycle identity verification akin to a biometric fingerprint.
  arXiv  Detail & Related papers  (2025-09-30T15:34:08Z)
• Fingerprinting LLMs via Prompt Injection [16.907123772391213]
  Large language models (LLMs) are often modified after release through post-processing such as post-training or quantization.<n>Existing provenance detection methods have two main limitations: (1) they embed signals into the base model before release, which is infeasible for already published models, or (2) they compare outputs across models using hand-crafted or random prompts, which are not robust to post-processing.<n>We propose LLMPrint, a novel detection framework that constructs fingerprints by exploiting LLMs' inherent vulnerability to prompt injection.
  arXiv  Detail & Related papers  (2025-09-29T19:54:36Z)
• SoK: Large Language Model Copyright Auditing via Fingerprinting [69.14570598973195]
  We introduce a unified framework and formal taxonomy that categorizes existing methods into white-box and black-box approaches.<n>We propose LeaFBench, the first systematic benchmark for evaluating LLM fingerprinting under realistic deployment scenarios.
  arXiv  Detail & Related papers  (2025-08-27T12:56:57Z)
• Gradient-Based Model Fingerprinting for LLM Similarity Detection and Family Classification [6.008384763761687]
  Large Language Models (LLMs) are integral software components in modern applications.<n>We presentGuard, a gradient-based fingerprinting framework for similarity detection and family classification.<n>Our approach extracts model-intrinsic behavioral signatures by analyzing responses to random input perturbations.<n>It supports the widely-adopted safetensors format and constructs high-dimensional fingerprints through statistical analysis of gradient features.
  arXiv  Detail & Related papers  (2025-06-02T13:08:01Z)
• Defending against Indirect Prompt Injection by Instruction Detection [109.30156975159561]
  InstructDetector is a novel detection-based approach that leverages the behavioral states of LLMs to identify potential IPI attacks.<n>InstructDetector achieves a detection accuracy of 99.60% in the in-domain setting and 96.90% in the out-of-domain setting, and reduces the attack success rate to just 0.03% on the BIPIA benchmark.
  arXiv  Detail & Related papers  (2025-05-08T13:04:45Z)
• HuRef: HUman-REadable Fingerprint for Large Language Models [44.9820558213721]
  HuRef is a human-readable fingerprint for large language models.<n>It uniquely identifies the base model without interfering with training or exposing model parameters to the public.
  arXiv  Detail & Related papers  (2023-12-08T05:01:47Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.