TRACE: A Two-Channel Robust Attribution Watermark via Complementary Embeddings for LLM-Agent Trajectories
arXiv:2607.08400v1 Announce Type: cross Abstract: LLM agents reach users through resellers, who may rebrand a developer's agent or substitute a cheaper model. When provenance is disputed, attribution rests on the trajectory log (the record of tool calls, observations, and executed actions, not the model's reasoning), which the reseller stores and processes to meter usage. A watermark must therefore survive an adversary with full read/write access to the very evidence it is detected from; existing agent watermarks do not, as their attribution is read straight off that log. We present TRACE, to
The proliferation of LLM agents and their deployment through various intermediaries necessitates robust attribution methods, especially as disputes over provenance and usage metering arise.
This development addresses a critical vulnerability in the LLM agent ecosystem, enabling developers to protect their intellectual property and ensure proper attribution amidst opaque reseller channels and potential model substitutions.
The TRACE watermark provides a novel method for proving the provenance of LLM agent trajectories, even when adversaries have full access to logs, significantly enhancing trust and accountability in the agent distribution chain.
- · LLM developers
- · Agent platforms
- · Users seeking authenticated agent services
- · IP attorneys
- · Malicious resellers
- · Unauthorized model substitutors
- · Entities engaging in IP theft within the agent ecosystem
Increased trust and security for LLM developers deploying agents through third parties.
Accelerated adoption of LLM agents in sensitive business applications due to enhanced accountability and provable provenance.
The emergence of new legal frameworks and industry standards for AI agent attribution and intellectual property rights, potentially leading to more complex contractual agreements around AI service provision.
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