# ChronosAttack: Adversarial Tool Scheduling Attacks on LLM Agents

arXiv: https://arxiv.org/abs/2609.27857 · HTML: https://mlsanity.com/paper/2609.27857/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Agent threats, guardrails and governance, one of 16 directions in LLM agents and tool use.
- Builds on 19 of its 24 references that are in the map.
- First posted on arXiv 20 Aug 2026.

## Placed in

- LLM agents and tool use / [Agent threats, guardrails and governance](https://mlsanity.com/agents/agent-threats-guardrails-and-governance/)

## Builds on (most cited first, 19 of 19)

- [ReAct: Synergizing Reasoning and Acting in Language Models](https://mlsanity.com/paper/2210.03629/) (2022)
- [Toolformer: Language Models Can Teach Themselves to Use Tools](https://mlsanity.com/paper/2302.04761/) (2023)
- [Not what you've signed up for: Compromising Real-World LLM-Integrated Applications with Indirect Prompt Injection](https://mlsanity.com/paper/2302.12173/) (2023)
- [InjecAgent: Benchmarking Indirect Prompt Injections in Tool-Integrated Large Language Model Agents](https://mlsanity.com/paper/2403.02691/) (2024)
- [Large Language Models Sensitivity to The Order of Options in Multiple-Choice Questions](https://mlsanity.com/paper/2308.11483/) (2023)
- [Prompt Injection Attack to Tool Selection in LLM Agents](https://mlsanity.com/paper/2504.19793/) (2025)
- [Memory Injection Attacks on LLM Agents via Query-Only Interaction](https://mlsanity.com/paper/2503.03704/) (2025)
- [Split and Merge: Aligning Position Biases in LLM-based Evaluators](https://mlsanity.com/paper/2310.01432/) (2023)
- [From Prompt Injections to Protocol Exploits: Threats in LLM-Powered AI Agents Workflows](https://mlsanity.com/paper/2506.23260/) (2025)
- [AttriGuard: Defeating Indirect Prompt Injection in LLM Agents via Causal Attribution of Tool Invocations](https://mlsanity.com/paper/2603.10749/) (2026)
- [From Untrusted Input to Trusted Memory: A Systematic Study of Memory Poisoning Attacks in LLM Agents](https://mlsanity.com/paper/2606.04329/) (2026)
- [AgentSentry: Mitigating Indirect Prompt Injection in LLM Agents via Temporal Causal Diagnostics and Context Purification](https://mlsanity.com/paper/2602.22724/) (2026)
- [The Attack and Defense Landscape of Agentic AI: A Comprehensive Survey](https://mlsanity.com/paper/2603.11088/) (2026)
- [Breaking ReAct Agents: Foot-in-the-Door Attack Will Get You In](https://mlsanity.com/paper/2410.16950/) (2024)
- [Async Control: Stress-testing Asynchronous Control Measures for LLM Agents](https://mlsanity.com/paper/2512.13526/) (2025)
- [MalTool: Malicious Tool Attacks on LLM Agents](https://mlsanity.com/paper/2602.12194/) (2026)
- [ConformalShift: Targeted Event Reordering Against Adaptive ECG Monitoring](https://mlsanity.com/paper/2608.03628/) (2026)
- [ToolHazard: Scaling Adversarial Environments for Security Evaluation and Alignment of LLM-based Agents](https://mlsanity.com/paper/2608.11878/) (2026)
- [CohortHijack: Robustness of Single Cell Annotation to Companion Cell Removal](https://mlsanity.com/paper/2608.05900/) (2026)

## Abstract (arXiv)

Large language model (LLM) agents often process external tool responses as they arrive, making response timing part of the decision process. We introduce ChronosAttack, a delay-only scheduling attack that changes when authentic tool responses arrive without modifying, adding, removing, or accelerating them. Bounded delays can change the order of the same evidence and alter the final decision. We evaluate ChronosAttack on GPT-5.6 Sol, Gemini 3.6 Flash, DeepSeek V4 Flash, and Claude Sonnet 4.6. GPT-5.6 Sol and Claude show strong targeted shifts in vulnerable settings, Gemini shows large shifts in the opposite direction, and DeepSeek is more stable under the tested schedules. We also find that sequential agent state is not always required and that a single scheduling inversion can cause a large decision change. Synchronization and order-consistency defenses reduce attacker control over observation order. These results show that tool-response timing can itself form an attack surface in asynchronous LLM agents.