arXiv:2605.08876v2 Announce Type: replace Abstract: Large Language Models (LLMs) are increasingly deployed as autonomous agents that execute tool-augmented, multi-step tasks, where latency is a critical factor for real-world applications. Yet an overlooked threat is Reasoning-Level Denial-of-Service (R-DoS), in which an attacker preserves task correctness but degrades availability by inflating an agent's reasoning depth or tool-use budget. We introduce OTora, the first unified, two-stage red-teaming framework for instantiating R-DoS attacks. Stage I optimizes an adversarial trigger that induce
As Large Language Models increasingly transition from static models to autonomous agents operating in real-world scenarios, vulnerabilities like Reasoning-Level Denial-of-Service become critical attack vectors.
This research highlights a new class of attacks that specifically target the operational availability of AI agents, which is paramount for their widespread and reliable deployment in critical applications.
The understanding of AI agent security expands beyond traditional data poisoning or adversarial input, now encompassing resource degradation through intelligent, subtle manipulation of reasoning processes.
- · AI red teamers
- · AI cybersecurity firms
- · Developers of robust LLM architectures
- · Unsecured LLM agent deployments
- · Organisations reliant on uninterrupted AI agent services without robust R-DoS mi
Immediate industry focus will shift towards developing defensive mechanisms and testing protocols against R-DoS attacks.
New security-by-design principles will be integrated into future autonomous AI agent development, increasing initial development costs but improving long-term reliability.
The R-DoS threat could force a re-evaluation of the risk profiles for deploying AI agents in high-stakes, real-time operational environments, potentially slowing adoption in some sectors.
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Read at arXiv cs.LG