arXiv:2607.06009v1 Announce Type: cross Abstract: Attributing code to the large language model that produced it is essential for provenance, licensing, and misuse accountability, yet no deployed watermark meets this need. Generation-time schemes require access to the producing model and cannot be applied to third-party code, while post-hoc schemes work on any code but carry at most 4 bits of payload, far too few to distinguish the many deployed model configurations. We present multi-channel spread-spectrum watermarking, the first post-hoc, training-free code watermark with a 24-bit payload and
The proliferation of AI-generated code and the increasing need for accountability, licensing enforcement, and misuse attribution are driving urgent demand for effective watermarking solutions.
This breakthrough addresses a critical gap in AI provenance, enabling better regulation, intellectual property protection, and security protocols for code generated by large language models.
The ability to post-hoc watermark AI-generated code with a significant payload fundamentally alters the potential for auditing, tracking, and managing the increasing volume of such code.
- · AI developers
- · Software companies
- · Governments/Regulators
- · Cybersecurity firms
- · Malicious actors
- · Plagiarists
- · Undocumented AI code producers
Improved accountability and intellectual property enforcement for code produced by large language models.
New business models emerge around AI code auditing, licensing, and compliance services.
Enhanced trust in AI-generated code, accelerating its adoption in sensitive or critical applications while also creating new vectors for undetectable, sophisticated tampering.
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Read at arXiv cs.LG