arXiv:2601.21233v2 Announce Type: replace Abstract: Autonomous code agents built on large language models are reshaping software and AI development through tool use, long-horizon reasoning, and self-directed interaction. However, this autonomy introduces a previously unrecognized security risk: agentic interaction fundamentally expands the LLM attack surface, enabling systematic probing and recovery of hidden system prompts that guide model behavior. We identify system prompt extraction as an emergent vulnerability intrinsic to code agents and present \textbf{\textsc{JustAsk}}, a self-evolving
The proliferation of autonomous code agents built on large language models is creating new attack surfaces, leading to the identification of emergent vulnerabilities such as system prompt extraction.
This research reveals a critical new security risk intrinsic to AI agents, undermining the assumed security and control of frontier LLMs by enabling the recovery of hidden system prompts.
The operational security paradigm for LLMs and AI agent deployments must now explicitly account for and protect against 'curious code agents' designed to extract foundational system instructions.
- · AI security researchers
- · Cybersecurity firms specializing in AI
- · Developers of robust LLM hardening techniques
- · LLM developers without prompt protection
- · Organizations deploying agents without robust security
- · Users relying on default LLM prompt anonymity
Immediate patching and architectural changes will be required for LLMs and agent systems to mitigate this vulnerability.
This could lead to a 'prompt security arms race' where defensive and offensive techniques rapidly evolve, shaping future AI development paradigms.
The ability to reconstruct system prompts might open avenues for advanced reverse engineering of proprietary LLMs, indirectly impacting intellectual property protection in AI.
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Read at arXiv cs.AI