arXiv:2607.00481v1 Announce Type: cross Abstract: Jailbreak attacks remain a critical threat to the safe deployment of large language models (LLMs). While prior work has primarily studied attacks and defenses at the prompt level, we show that this prompt-centric paradigm overlooks a structural vulnerability in stateful, function-calling environments. In such applications, developer-defined schemas, structured arguments, and untrusted tool outputs are interleaved into a single shared model context. This architecture expands the attack surface by blurring the boundary between trusted control log
The rapid deployment of function-calling LLMs into production environments, coupled with increasing sophistication in attack vectors, means new vulnerabilities are actively being discovered and exploited.
Organizations relying on function-calling LLMs for stateful applications face significant security risks, demanding immediate attention to novel jailbreaking methods that bypass traditional prompt-level defenses.
The focus of LLM security shifts from solely prompt-level defenses to a broader consideration of the entire application architecture, including developer-defined schemas and untrusted tool outputs.
- · Cybersecurity firms specializing in AI
- · Developers focused on secure LLM architectures
- · AI red teaming specialists
- · Researchers in LLM safety
- · Organizations deploying insecure function-calling LLMs
- · LLM application developers without robust security practices
- · Users of compromised AI systems
Increased investment in specialized security protocols and frameworks for function-calling LLMs will occur.
New industry standards and regulatory guidelines for AI application security, particularly for stateful systems, will emerge.
The development and adoption of 'AI security by design' principles will accelerate across the software development lifecycle for AI-powered applications.
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Read at arXiv cs.AI