mlsanityData to 14 Oct 2026

Persistent Billable State: Denial-of-Wallet Attacks and Defenses in Tool-Calling LLM Agents

arXiv 2609.28585 ↗First posted 23 Sep 2026cs.CR cs.AIcited by 0

On the map: LLM agents and tool use → Agent threats, guardrails and governance

Abstract (arXiv)

Multi-step tool-calling LLM agents rely on host runtimes to preserve state across turns. When a runtime carries an external tool return into later model inputs, providers meter it again. An admitted malicious or compromised tool can thereby convert untrusted data into recurring victim-billed processing without victim credentials or local runtime privilege. We call retained content persistent billable state and formalize the host's decision over whether and how it enters later billable context as the persistent billable-state boundary. We present the first systematic security study of this post-admission lifecycle. We derive six denial-of-wallet attack vectors and build DOW-BENCH, an end-to-end harness evaluated across six model families. Across 243 executions, usage telemetry shows that the maximum per-session cumulative input reaches 14,293x the session's first-call input. Controlled history-policy reruns isolate raw retention's contribution: retaining raw history increases mean effective session cost by 21.2-35.9%. Compression succeeds on 10/12 and 11/12 history-dependent tasks, versus 2/12 under deletion for each provider. To govern this boundary, we combine deterministic history transformation with four host-side invariants that bound prompt mass, context growth, recursive opportunity, and cumulative spend before reingestion. The kernel contains every recurring attack in the 123-evaluation replay corpus. Across 24 Mistral Small 4 workflows, a progress-authorized policy achieves 22/24 oracle-verified task successes with no pre-completion interruptions, versus 13/24 under a fixed cap. Only 71 of 3,830 scanned MCP server and transport repositories expose any code-visible safeguard proxy, and none cover all four safeguard families. These results establish persistent billable state as a first-class security object and pre-reingestion as its host-owned control point.

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 34 of its 50 references that are in the map. First posted on arXiv 23 Sep 2026.

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34 of 50 references.

Closest ancestors · 14

Same direction, newest first

  1. 2026From Shield to Target: Denial-of-Service Attacks on LLM-Based Agent Guardrails12 Jun
  2. 2026Clawdrain: Exploiting Tool-Calling Chains for Stealthy Token Exhaustion in OpenClaw Agents1 Mar
  3. 2026Skill-Inject: Measuring Agent Vulnerability to Skill File Attacks23 Feb
  4. 2026AgentLAB: Benchmarking LLM Agents against Long-Horizon Attacks18 Feb
  5. 2026Overthinking Loops in Agents: A Structural Risk via MCP Tools16 Feb
  6. 2026The Landscape of Prompt Injection Threats in LLM Agents: From Taxonomy to Analysis11 Feb
  7. 2026Beyond Max Tokens: Stealthy Resource Amplification via Tool Calling Chains in LLM Agents16 Jan
  8. 2025LeechHijack: Covert Computational Resource Exploitation in Intelligent Agent Systems2 Dec
  9. 2025MCP Security Bench (MSB): Benchmarking Attacks Against Model Context Protocol in LLM Agents14 Oct
  10. 2025Design Patterns for Securing LLM Agents against Prompt Injections10 Jun
  11. 2024AgentHarm: A Benchmark for Measuring Harmfulness of LLM Agents11 Oct
  12. 2024InjecAgent: Benchmarking Indirect Prompt Injections in Tool-Integrated Large Language Model Agents5 Mar
2 more
  1. 2024Agent Smith: A Single Image Can Jailbreak One Million Multimodal LLM Agents Exponentially Fast13 Feb
  2. 2023Identifying the Risks of LM Agents with an LM-Emulated Sandbox25 Sep

Other references · 20

  1. 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,695
  2. 2023Toolformer: Language Models Can Teach Themselves to Use Toolscited by 4,182
  3. 2023A Survey on Large Language Model based Autonomous Agentscited by 2,242
  4. 2023ToolLLM: Facilitating Large Language Models to Master 16000+ Real-world APIscited by 1,947
  5. 2023Not what you've signed up for: Compromising Real-World LLM-Integrated Applications with Indirect Prompt Injectioncited by 1,443
  6. 2023The Rise and Potential of Large Language Model Based Agents: A Surveycited by 1,439
  7. 2023Gorilla: Large Language Model Connected with Massive APIscited by 1,332
  8. 2023FrugalGPT: How to Use Large Language Models While Reducing Cost and Improving Performancecited by 853
  9. 2024RouteLLM: Learning to Route LLMs with Preference Datacited by 682
  10. 2023Prompt Injection attack against LLM-integrated Applicationscited by 605
  11. 2024Hybrid LLM: Cost-Efficient and Quality-Aware Query Routingcited by 372
  12. 2023LLMLingua: Compressing Prompts for Accelerated Inference of Large Language Modelscited by 287
8 more
  1. 2024A New Era in LLM Security: Exploring Security Concerns in Real-World LLM-based Systemscited by 77
  2. 2025ThinkTrap: Denial-of-Service Attacks against Black-box LLM Services via Infinite Thinkingcited by 23
  3. 2026State of AI: An Empirical 100 Trillion Token Study with OpenRoutercited by 18
  4. 2026Agent Contracts: A Formal Framework for Resource-Bounded Autonomous AI Systemscited by 16
  5. 2026Token Budgets: An Empirical Catalog of 63 LLM-Agent Budget-Overrun Incidents, with an Affine-Typed Rust Mitigation as a Case Studycited by 6
  6. 2015DDoS Attacks in Cloud Computing: Issues, Taxonomy, and Future Directionscited by 3
  7. 2021Denial of Wallet -- Defining a Looming Threat to Serverless Computingcited by 2
  8. 2026Talk is (Not) Cheap: A Taxonomy and Benchmark Coverage Audit for LLM Attackscited by 1

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  8. 2026Stale Does Not Mean Unsafe: Guard Precision for Tool-Using LLM Agents under Infrastructure State Races