Bipartisan Bill Seeks Federal Playbook to Shield AI Models from Distillation Attacks

A coalition of Republicans and Democrats in the House has introduced the AI Cyber Defense Act, a bill that would task the federal government with producing official guidance on protecting artificial‑intelligence models from a growing class of threats known as distillation attacks.

Distillation attacks involve extracting the knowledge embedded in a proprietary AI model by feeding it large volumes of data and then using the outputs to reconstruct a comparable model. The technique can undermine intellectual property, enable malicious actors to bypass safety controls, and accelerate the spread of unverified AI capabilities. Experts say the practice is becoming more feasible as powerful language models proliferate and as cloud‑based inference services expand.

The legislation, co‑sponsored by Rep. Pat Harrigan, a Republican from North Carolina, and a Democratic colleague whose name has not been disclosed, calls for the development of a comprehensive set of defensive strategies. The guidance would be crafted in consultation with industry stakeholders, academic researchers, and cybersecurity agencies, and would outline best practices for model watermarking, query‑rate limiting, and secure deployment architectures.

Lawmakers framed the proposal against a backdrop of mounting concerns about AI security. In recent months, several high‑profile incidents have highlighted the vulnerability of commercial models to extraction attempts, prompting calls from the National Institute of Standards and Technology and the Department of Commerce for clearer policy direction. The AI Cyber Defense Act aims to fill that gap by establishing a formal, government‑backed framework rather than relying on ad‑hoc measures.

If enacted, the bill would also mandate a series of roundtables that bring together leading AI developers, cloud providers, and cyber‑defense firms. Those meetings are intended to surface practical challenges, share threat intelligence, and coordinate a unified response to model‑theft techniques. Advocates argue that a coordinated industry effort could accelerate the adoption of protective technologies that are currently scattered across private research labs.

The proposal is slated for referral to the House Committee on Energy and Commerce, where it is expected to receive bipartisan backing given the shared economic stakes in safeguarding AI investments. Critics, however, caution that overly prescriptive regulations could stifle innovation or impose burdens on smaller startups lacking extensive security resources. The bill’s sponsors have signaled openness to iterative development of the guidance, allowing the standards to evolve alongside the rapidly changing AI landscape.

As the conversation around AI risk matures, the AI Cyber Defense Act represents one of the first legislative attempts to codify defensive measures against model extraction. Its progress through Congress will likely serve as a bellwether for how quickly policymakers can translate technical threats into actionable, industry‑wide safeguards.