TrajMark: Ownership Attribution and Segment-Level Tamper Localization for Coding-Agent Trajectories
- URL: http://arxiv.org/abs/2609.10416v1
- Date: Wed, 09 Sep 2026 16:33:02 GMT
- Title: TrajMark: Ownership Attribution and Segment-Level Tamper Localization for Coding-Agent Trajectories
- Abstract summary: TrajMark is a training-free, symmetric-key, visible-only trajectory watermarking framework.<n>It separates robust ownership attribution from fragile local integrity verification.<n>It recovers the exact owner in all evaluated clean full-watermark batches.
- Score: 6.234219482022044
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Watermarking the final patch produced by a coding agent provides provenance evidence for the submitted artifact, but does not authenticate the visible process that produced it. Behavioral watermarking methods primarily provide a global detection or identifier-recovery signal, so a locally edited trajectory may retain sufficient ownership evidence without revealing which protected region has become inconsistent. To address this limitation, we propose TrajMark, a training-free, symmetric-key, visible-only trajectory watermarking framework that separates robust ownership attribution from fragile local integrity verification. Our framework consists of two complementary layers: a sparse owner layer that encodes a six-bit deployment identifier by rewriting a keyed subset of naturally occurring READ actions into masked linear equations, and a localization layer that inserts linked Q12 ordinary, group, and terminal seals to commit to protected critical-action segments. This separation allows ownership evidence to accumulate robustly across trajectories, while local modifications perturb nearby keyed commitments and expose the affected protocol region. We further provide a design-level analysis of owner recoverability, integrity collision probability, structural overhead, and localization behavior. Across three coding-agent frameworks and three LLMs, TrajMark recovers the exact owner in all evaluated clean full-watermark batches. Under exhaustive eligible single-site attacks it detects 95.5%-100% of edits, and under random single-action corruption it localizes 95.8% of modified sites to an accepted protocol region rather than to the individual action. Owner marking adds no trajectory actions; the integrity layer adds explicit read-only seals, and matched Pass@1 is 26.9% versus 26.3% for unwatermarked runs.
Related papers
- Privacy-Preserving Topology-Guided Safety for LLM-Based Multi-Agent Systems via Federated Graph Learning [67.04448659688579]
FGLGuard cuts AgentDojo's ground-success rate by 43% at near-unguarded utility, zero API cost, and negligible capability loss.<n>Federation is not optional: off-the-shelf transfer collapses under distribution shift.<n>Live FGLGuard cuts AgentDojo's ground-success rate by 43% at near-unguarded utility, zero API cost, and negligible capability loss.
arXiv Detail & Related papers (2026-09-02T07:57:12Z) - Tracing Provenance and Detecting Tampering with Complementary LLM Watermarks [68.64050157343334]
Existing watermarks preserve provenance under editing, but this same robustness allows an adversary to alter critical content while retaining attribution.<n>We introduce an innovative watermark that jointly provides provenance and tamper evidence.<n>The signals share the same mechanism but use independent keys and different seeding windows over normalized text, making one resilient to edits and the other sensitive to reader-visible changes.
arXiv Detail & Related papers (2026-08-13T01:55:16Z) - RedAct: Redacting Agent Capability Traces for Procedural Skill Protection [6.305785618804429]
Execution traces contain rich procedural detail, including tool invocations, intermediate decisions, and error-recovery logic.<n>This detail can expose private procedural skills, allowing downstream methods to recover key formulas, thresholds, and strategies without access to model weights or skill files.<n>We introduce RedAct, a protected trace release framework that localizes protected key information, rewrites traces while preserving verifier-critical evidence, and embeds behavioral watermarks for downstream provenance analysis.
arXiv Detail & Related papers (2026-06-09T12:57:50Z) - Sequential Behavioral Watermarking for LLM Agents [6.193603972930103]
LLM-based agents act through sequences of executable decisions, but their trajectories provide little evidence of which agent or policy produced them.<n>This motivates watermarking signals embedded directly into agent behavior rather than only into generated text.<n>We propose SeqWM, a sequential behavioral watermarking framework that embeds signals into history-conditioned transition patterns.
arXiv Detail & Related papers (2026-05-11T02:28:34Z) - All in One: Unifying Deepfake Detection, Tampering Localization, and Source Tracing with a Robust Landmark-Identity Watermark [25.001768538619558]
Malicious face manipulations pose a significant threat to personal privacy and social security.<n>Existing proactive forensics methods treat deepfake detection, tampering localization, and source tracing as independent tasks.<n>We propose a unified proactive forensics framework that jointly addresses these three core tasks.
arXiv Detail & Related papers (2026-02-26T21:57:15Z) - MerkleSpeech: Public-Key Verifiable, Chunk-Localised Speech Provenance via Perceptual Fingerprints and Merkle Commitments [0.0]
We propose MerkleSpeech, a system for public-key verifiable, chunk-localised speech provenance.<n>The system computes perceptual fingerprints over short speech chunks, commits them in a Merkle tree whose root is signed with an issuer key.<n>We present experiments targeting very low false positive rates under resampling, bandpass filtering, and additive noise.
arXiv Detail & Related papers (2026-02-10T11:58:19Z) - StableGuard: Towards Unified Copyright Protection and Tamper Localization in Latent Diffusion Models [55.05404953041403]
We propose a novel framework that seamlessly integrates a binary watermark into the diffusion generation process.<n>We show that StableGuard consistently outperforms state-of-the-art methods in image fidelity, watermark verification, and tampering localization.
arXiv Detail & Related papers (2025-09-22T16:35:19Z) - Towards Copyright Protection for Knowledge Bases of Retrieval-augmented Language Models via Reasoning [58.57194301645823]
Large language models (LLMs) are increasingly integrated into real-world personalized applications.<n>The valuable and often proprietary nature of the knowledge bases used in RAG introduces the risk of unauthorized usage by adversaries.<n>Existing methods that can be generalized as watermarking techniques to protect these knowledge bases typically involve poisoning or backdoor attacks.<n>We propose name for harmless' copyright protection of knowledge bases.
arXiv Detail & Related papers (2025-02-10T09:15:56Z) - Lazy Layers to Make Fine-Tuned Diffusion Models More Traceable [70.77600345240867]
A novel arbitrary-in-arbitrary-out (AIAO) strategy makes watermarks resilient to fine-tuning-based removal.
Unlike the existing methods of designing a backdoor for the input/output space of diffusion models, in our method, we propose to embed the backdoor into the feature space of sampled subpaths.
Our empirical studies on the MS-COCO, AFHQ, LSUN, CUB-200, and DreamBooth datasets confirm the robustness of AIAO.
arXiv Detail & Related papers (2024-05-01T12:03:39Z) - Cycle Label-Consistent Networks for Unsupervised Domain Adaptation [57.29464116557734]
Domain adaptation aims to leverage a labeled source domain to learn a classifier for the unlabeled target domain with a different distribution.
We propose a simple yet efficient domain adaptation method, i.e. Cycle Label-Consistent Network (CLCN), by exploiting the cycle consistency of classification label.
We demonstrate the effectiveness of our approach on MNIST-USPS-SVHN, Office-31, Office-Home and Image CLEF-DA benchmarks.
arXiv Detail & Related papers (2022-05-27T13:09:08Z)
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.