arXiv:2606.07968v1 Announce Type: cross Abstract: Reasoning-capable large language models can be induced to spend their generation budget on injected decoy tasks rather than answering the user's question, causing denial of service when no final answer is produced and denial of wallet when excess output tokens are billed. Input-side safety classifiers often miss these attacks because the injected prompts can appear syntactically benign. We build RecurGuard, a runtime monitor for detecting reasoning-chain consumption attacks when reasoning traces are exposed by the model. RecurGuard analyzes rea
The increasing deployment of reasoning-capable large language models (LLMs) makes the economic and security implications of 'denial of wallet' and 'denial of service' attacks more pressing, driving the need for real-time protection.
This development highlights a critical vulnerability in the operational security and cost-effectiveness of LLM-powered applications, directly impacting their commercial viability and reliability for users.
The introduction of runtime monitoring specifically for reasoning-chain consumption attacks shifts the focus from purely input-side prompt filtering to active, real-time observation of model behavior for security and resource management.
- · AI security vendors
- · Enterprises deploying LLMs
- · Cloud providers offering AI services
- · Attackers exploiting LLM vulnerabilities
- · Users experiencing denial of service/wallet
- · LLM developers without robust security features
Wider adoption of similar runtime security measures will become standard for foundational models and agents.
This could lead to a 'red team' vs 'blue team' arms race in LLM security, driving innovations in both attack and defense.
Improved LLM security may accelerate the deployment of autonomous AI agents in sensitive applications as trust in their operational integrity grows.
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Read at arXiv cs.AI