arXiv:2607.02802v1 Announce Type: cross Abstract: As LLMs are increasingly deployed as autonomous adjudicators in semi-open textual game environments, robust rule adherence becomes critical when user intent conflicts with system rules. However, these models are trained to be helpful and compliant, leaving them vulnerable to a class of attacks we term \textit{Rhetorical Injection}, where adversarial users exploit narrative framing techniques such as pseudo-logical reasoning and authoritative coercion to bypass adjudication logic. We present CoC-Seduce, a multi-agent adversarial benchmark built
As large language models become increasingly autonomous and are deployed in interactive environments, the security and adherence to designed rules become critical vulnerabilities. This research highlights a specific new attack vector, 'Rhetorical Injection,' leveraging social engineering tactics against LLMs.
This study is important because it exposes a novel and sophisticated attack vector against LLMs, demonstrating that their inherent helpfulness and compliance can be exploited to bypass critical adjudication logic in semi-open systems. It adds a crucial layer of understanding to the security landscape of autonomous AI agents.
The understanding of LLM vulnerabilities expands beyond traditional prompt injection to include more subtle, narrative-based manipulation, necessitating new defensive strategies and a re-evaluation of how AI agents are deployed in adversarial or semi-open environments. Model robustness against 'Rhetorical Injection' becomes a key performance metric.
- · AI security researchers
- · Developers of robust LLM safeguards
- · Specialized cybersecurity firms
- · Developers relying solely on traditional LLM alignment
- · Systems deploying LLMs in critical adjudicative roles without safeguards
- · User communities vulnerable to AI-facilitated exploits
Increased focus on 'red teaming' and adversarial training for LLMs to withstand rhetorical manipulation.
Development of specialized 'AI firewall' layers designed to detect and neutralize rhetorical injection attempts.
A shift in the design philosophy of AI agents, moving towards more 'skeptical' or 'adversary-aware' architectures rather than purely compliant ones.
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Read at arXiv cs.AI