RedAct: Redacting Agent Capability Traces for Procedural Skill Protection

Problem
This work addresses the vulnerability of agent execution traces, which contain sensitive procedural details that can be exploited to recover proprietary skills and strategies. The authors highlight a gap in the literature regarding the protection of these traces, which are essential for diagnosing failures and ensuring accountability. The paper is a preprint and has not undergone peer review.

Method
The authors propose \textsc{RedAct}, a framework designed to redact sensitive information from execution traces while preserving critical evidence for verification. The framework operates by localizing key information that needs protection, rewriting the traces to obscure this information, and embedding behavioral watermarks to facilitate provenance analysis. The evaluation is conducted using \textsc{CapTraceBench}, a benchmark comprising 75 specialized long-horizon tasks and 154 curated skills across seven domains. The authors do not disclose specific architectural details or training compute but focus on the effectiveness of the redaction process.

Results
\textsc{RedAct} demonstrates a significant reduction in normalized skill transfer (NST) across various trace reuse methods, achieving a decrease from 44.7% to 67.1% on raw traces to below the no-skill baseline. Additionally, the behavioral watermarks embedded in the traces achieve a true detection rate of 93.6% to 100.0%, with a false alarm rate capped at 1.9%. These results indicate that the framework effectively mitigates the risk of procedural capability leakage while maintaining the integrity of audit evidence.

Limitations
The authors acknowledge that while \textsc{RedAct} effectively reduces skill transfer, the framework’s performance may vary across different domains and tasks, which could limit its generalizability. They also do not address potential computational overhead introduced by the redaction process or the scalability of the watermarking technique in real-world applications. Furthermore, the reliance on behavioral watermarks may raise concerns regarding their robustness against adversarial attacks.

Why it matters
The implications of this work are significant for the field of AI and agent-based systems, particularly in contexts where procedural skills are proprietary and sensitive. By framing public agent traces as security interfaces, the authors provide a novel approach to safeguarding intellectual property while ensuring accountability through audit evidence. This research opens avenues for further exploration into secure agent design and trace management, as highlighted in related works on procedural skill protection and accountability in AI systems, as published in arXiv.