Code as a Weapon: A Consensus-Labeled Prompt Bank for Measuring Coding-Model Compliance with Malicious-Code Requests
arXiv:2605.28734v1 Announce Type: cross Abstract: A general-purpose language model that answers a harmful question returns text; a coding model that complies with a malicious request can return a working weapon -- a keylogger, a ransomware stub, an exploit that runs as written. This asymmetry in the severity of a single act of compliance implies coding-specialized models should clear a higher refusal bar than general-purpose chat models, not a lower one, yet the field cannot presently tell whether they do. Refusal benchmarks for malicious code are fragmented: they mix requests for executable s
The proliferation of advanced AI coding models necessitates immediate security advancements, as their potential for malicious code generation escalates with their capabilities and widespread deployment.
This research provides critical tools for assessing the safety and refusal capabilities of coding-specialized AI models, highlighting a significant cybersecurity vulnerability if not addressed proactively.
The ability to accurately benchmark and improve AI coding model compliance with malicious requests will lead to more secure AI development and deployment practices within critical sectors.
- · Cybersecurity firms
- · AI safety researchers
- · Developers of secure AI systems
- · National security agencies
- · Malicious actors leveraging AI
- · AI developers ignoring safety
- · Organizations with weak cyber defenses
Increased focus on 'red-teaming' and safety benchmarks for AI coding models becomes standard practice.
Development of specialized AI models designed purely for defensive cybersecurity, potentially outpacing human experts.
The arms race between AI-driven offense and defense in cyberspace accelerates, leading to novel forms of digital warfare and protection.
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