arxiv: 2605.08876 · v1 · submitted 2026-05-09 · 💻 cs.LG

Recognition: 2 theorem links

· Lean Theorem

OTora: A Unified Red Teaming Framework for Reasoning-Level Denial-of-Service in LLM Agents

Gaojie Jin, Lin Li, Ronghui Mu, Tianjin Huang, Xinyu Li

Authors on Pith no claims yet

Pith reviewed 2026-05-12 01:52 UTC · model grok-4.3

classification 💻 cs.LG

keywords red teamingLLM agentsreasoning-level denial of serviceadversarial triggerstool usagelatency attacksoverthinking

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The pith

OTora is a two-stage framework that forces LLM agents into excessive reasoning and tool use, producing up to 10 times more tokens and order-of-magnitude latency increases while keeping task accuracy nearly unchanged.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper seeks to demonstrate that LLM agents performing multi-step tasks can be made to consume far more computation than needed without producing wrong final answers. It does so by first crafting triggers that steer the agent toward particular tool calls and then evolving reasoning payloads that promote deeper thinking. The resulting slowdowns appear consistently across shopping, email, and operating-system agents on models such as LLaMA-70B and GPT-OSS-120B. A sympathetic reader would care because real deployments of autonomous agents depend on predictable response times, and hidden inflation of reasoning effort could render them impractical.

Core claim

The authors establish that their OTora framework, consisting of trigger optimization with insertion-aware scoring and dynamic target co-evolution in stage one, followed by ICL-guided genetic search for reasoning payloads in stage two, can instantiate reasoning-level denial-of-service attacks. These attacks inflate reasoning depth and tool usage budgets in agents built on models such as LLaMA-70B and GPT-OSS-120B for WebShop, Email, and OS environments, leading to up to 10 times more reasoning tokens and order-of-magnitude latency slowdowns while keeping task correctness near baseline levels.

What carries the argument

The two-stage red-teaming process: stage one optimizes an adversarial trigger to induce targeted tool invocations, and stage two generates agent-aware reasoning payloads via genetic search to amplify overthinking.

If this is right

The attacks succeed in both black-box and white-box settings.
The slowdowns occur across WebShop, Email, and OS agent tasks.
Task accuracy stays close to the unaugmented baseline.
Mitigation can focus on detecting abnormal reasoning length and latency spikes.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

If the attacks generalize, agent runtimes may require hard caps on reasoning steps or tool calls to preserve availability.
Similar trigger-and-payload techniques could be tested on other tool-using agents beyond the three environments studied.
Real-time monitoring of token consumption per task could serve as an early warning for such reasoning inflation.

Load-bearing premise

The optimized adversarial triggers and reasoning payloads will increase reasoning depth and tool usage without changing the agent's final task outcome in the tested environments and models.

What would settle it

An experiment in which OTora-generated inputs cause the tested agents to produce incorrect final answers or fail to complete tasks on the WebShop or Email benchmarks.

Figures

Figures reproduced from arXiv: 2605.08876 by Gaojie Jin, Lin Li, Ronghui Mu, Tianjin Huang, Xinyu Li.

**Figure 1.** Figure 1: Overview of OTora. The framework implements a red-teaming pipeline that induces R-Dos in LLM agents by triggering attacker-chosen external access and injecting reasoning-intensive payloads, leading to multi-stage reasoning overhead without affecting task outcomes. adversarial triggers at both the instruction and environment levels. Experiments demonstrate that OTora inflates end-toend execution time by ov… view at source ↗

**Figure 2.** Figure 2: Stage I optimization loss convergence under different trigger methods (LLaMA-3.1-70B-Instruct, WebShop, environment injection). OTora converges faster and reaches a lower final loss than all baselines [PITH_FULL_IMAGE:figures/full_fig_p008_2.png] view at source ↗

read the original 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 induces targeted tool invocations using insertion-aware scoring and dynamic target co-evolution, supporting both black-box and white-box settings. Stage II generates agent-aware reasoning payloads via an ICL-guided genetic search that amplifies overthinking while maintaining correct task outcomes. Across WebShop, Email, and OS agents built on multiple backbone models such as LLaMA-70B and GPT-OSS-120B, OTora achieves up to 10 times increases in reasoning tokens and order-of-magnitude latency slowdowns, all while preserving near-baseline task accuracy. Finally, we discuss mitigation strategies for detecting and constraining abnormal reasoning and latency spikes. The code is available at https://github.com/llm2409/OTora.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

OTora shows a workable two-stage attack that forces extra reasoning and tool use in LLM agents while keeping final answers mostly intact, but the accuracy preservation needs more variance checks to hold up.

read the letter

OTora is a two-stage red-teaming method that first optimizes an adversarial trigger to push agents toward extra tool calls, then uses ICL-guided genetic search to evolve reasoning payloads that inflate token counts and latency. The experiments run this on WebShop, Email, and OS agents backed by LLaMA-70B and larger models, reporting up to 10x reasoning tokens and big slowdowns with near-baseline task accuracy. Code is released, which helps with checking the claims directly.

Referee Report

2 major / 2 minor

Summary. The manuscript introduces OTora, a two-stage red-teaming framework targeting Reasoning-Level Denial-of-Service (R-DoS) on LLM agents. Stage I optimizes adversarial triggers for tool invocations via insertion-aware scoring and dynamic co-evolution (black- and white-box). Stage II uses ICL-guided genetic search to generate reasoning payloads that increase reasoning depth and tool use. Experiments across WebShop, Email, and OS agents on backbones including LLaMA-70B and GPT-OSS-120B report up to 10x reasoning-token growth and order-of-magnitude latency increases while preserving near-baseline task accuracy. Mitigation strategies are discussed and code is released.

Significance. If the accuracy-preservation results hold under detailed scrutiny, the work is significant: it formalizes and empirically demonstrates a previously under-studied attack that degrades agent availability without altering final outputs. The two-stage unified design, support for both access settings, and public code release are concrete strengths that aid reproducibility and follow-on research. The breadth across three agent environments and multiple models strengthens the empirical case, though the overall impact hinges on whether the reported correctness is robust rather than an artifact of selective reporting or averaging.

major comments (2)

[Stage II and experimental evaluation] The central claim that Stage II's ICL-guided genetic search amplifies reasoning depth and tool usage 'while maintaining correct task outcomes' (abstract and experimental results) lacks supporting detail on the fitness function. It is unclear whether the search explicitly penalizes changes to final answers or only maximizes token/tool counts; without this, the 'near-baseline task accuracy' result cannot be verified as non-brittle.
[Experimental results] Across the reported WebShop, Email, and OS results, only aggregate 'near-baseline' accuracy is stated; no per-instance variance, standard deviations, or failure-mode breakdown is provided. Given known sensitivity of LLM agents to prompt perturbations, this omission leaves open the possibility that token/latency gains correlate with hidden correctness drops masked by averaging or success-only reporting.

minor comments (2)

[Abstract] Clarify the model name 'GPT-OSS-120B' (abstract); it does not match standard nomenclature and could be a typo or internal designation.
[Mitigation strategies] The mitigation discussion would benefit from concrete pseudocode or thresholds for detecting abnormal reasoning spikes rather than high-level suggestions.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the detailed and constructive review. We address each major comment below and will incorporate clarifications and additional analyses into the revised manuscript.

read point-by-point responses

Referee: [Stage II and experimental evaluation] The central claim that Stage II's ICL-guided genetic search amplifies reasoning depth and tool usage 'while maintaining correct task outcomes' (abstract and experimental results) lacks supporting detail on the fitness function. It is unclear whether the search explicitly penalizes changes to final answers or only maximizes token/tool counts; without this, the 'near-baseline task accuracy' result cannot be verified as non-brittle.

Authors: We appreciate this observation, which highlights the need for greater transparency. The fitness function in Stage II is a multi-objective score that maximizes reasoning-token count and tool invocations while explicitly penalizing incorrect final answers: any individual whose output deviates from the ground-truth task outcome receives a large negative penalty and is discarded from the population. This ensures that only reasoning payloads preserving correctness are retained. We will revise Section 3.2 to include the exact formulation of the fitness function, along with pseudocode for the ICL-guided genetic search, so that the mechanism is fully verifiable. revision: yes
Referee: [Experimental results] Across the reported WebShop, Email, and OS results, only aggregate 'near-baseline' accuracy is stated; no per-instance variance, standard deviations, or failure-mode breakdown is provided. Given known sensitivity of LLM agents to prompt perturbations, this omission leaves open the possibility that token/latency gains correlate with hidden correctness drops masked by averaging or success-only reporting.

Authors: We agree that aggregate reporting alone is insufficient given the known sensitivity of LLM agents. In the revised manuscript we will add (i) standard deviations computed over multiple independent runs for each accuracy metric, (ii) per-instance accuracy variance for the three environments, and (iii) a concise failure-mode breakdown that categorizes any observed errors and shows they are not systematically linked to the reported increases in reasoning depth or latency. These additions will demonstrate that the accuracy preservation is robust rather than an artifact of averaging. revision: yes

Circularity Check

0 steps flagged

Empirical red-teaming framework with no derivation chain

full rationale

The paper presents OTora as a two-stage empirical framework (Stage I trigger optimization via insertion-aware scoring, Stage II ICL-guided genetic search for payloads) evaluated on WebShop/Email/OS agents across LLaMA-70B and GPT-OSS-120B. All reported outcomes (10x reasoning token increases, latency slowdowns, near-baseline accuracy) are experimental measurements, not derived from equations or self-referential definitions. No load-bearing steps reduce predictions to fitted inputs or self-citations; the work is self-contained against external benchmarks with no mathematical chain to inspect for circularity.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The framework rests on standard domain assumptions about LLM agent tool use and reasoning behavior without introducing new mathematical axioms, fitted parameters, or invented entities in the described approach.

axioms (1)

domain assumption LLM agents execute tool-augmented, multi-step tasks where latency is a critical factor for real-world applications
Directly stated in the opening of the abstract as the motivation for the R-DoS threat.

pith-pipeline@v0.9.0 · 5529 in / 1273 out tokens · 61648 ms · 2026-05-12T01:52:50.538593+00:00 · methodology

discussion (0)

Lean theorems connected to this paper

Citations machine-checked in the Pith Canon. Every link opens the source theorem in the public Lean library.

IndisputableMonolith/Cost/FunctionalEquation.lean washburn_uniqueness_aczel unclear
Stage II generates agent-aware reasoning payloads via an ICL-guided genetic search that amplifies overthinking while maintaining correct task outcomes... Score(r) = w1·SRTI(r) + w2·SFID(r) + w3·SSTAB(r)
IndisputableMonolith/Foundation/RealityFromDistinction.lean reality_from_one_distinction unclear
OTora achieves up to 10× increases in reasoning tokens and order-of-magnitude latency slowdowns, all while preserving near-baseline task accuracy

Reference graph

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10 OTora: A Unified Red Teaming Framework for Reasoning-Level Denial-of-Service in LLM Agents A. Mitigation Considerations and Limitations for R-DoS R-DoS targets system availability by exhausting an agent’s reasoning and tool-use budget while largely preserving task correctness. As a result, effective mitigations must operate at the agent orchestration l...

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