Holding Agents Accountable with Verifiable Agent Insurance
Not on the map. Why?
Abstract (arXiv)
While AI agents are useful for many tasks, they regularly misbehave in destructive ways, such as deleting important data. While prior work has focused on preventing such misbehavior, we consider how to recover after an agent misbehaves. We propose verifiable agent insurance (VAI), a framework for agent providers to commit financially to promises, a list of programmatically-specified policies that an agent is meant to follow. These promises are embedded in a smart contract on an immutable ledger (e.g., a blockchain) to prevent tampering, along with a monetary escrow. A user who detects a violation of committed promises can prompt a verifier to review agent execution traces, which are logged through the agent harness's shared tool-execution path, without instrumenting each tool separately. Upon verification of the violation, the agent provider's escrow funds are paid to the reporting user. We instantiate VAI as an end-to-end implementation on an Ethereum layer-2 network, and evaluate its coverage on three prominent agent benchmarks, two of which we re-annotated to obtain task-level policy-compliance labels. Currently, many agents do not emit user authorizations and security-relevant information as explicit, structured data objects -- rather, they embed the data needed to verify policy violations in natural language exchanges. Even so, VAI manages to detect violations in 42.2% of runs containing violations across three benchmarks. By augmenting agents to explicitly emit such objects, violation detection rises to 83.0% of traces. Our findings suggest that for improved auditability, agents should be co-designed with their security policies to emit explicit, structured records of user authorization and security-relevant actions.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 9 of its 48 references that are in the map. First posted on arXiv 13 Oct 2026.
Builds on
9 of 48 references.
References · 9
- 2024τ-bench: A Benchmark for Tool-Agent-User Interaction in Real-World Domainscited by 1,402
- 2025Defeating Prompt Injections by Designcited by 297
- 2025AgentSpec: Customizable Runtime Enforcement for Safe and Reliable LLM Agentscited by 223
- 2025DRIFT: Dynamic Rule-Based Defense with Injection Isolation for Securing LLM Agentscited by 66
- 2026ClawsBench: Evaluating Capability and Safety of LLM Productivity Agents in Simulated Workspacescited by 33
- 2025Let It Flow: Agentic Crafting on Rock and Roll, Building the ROME Model within an Open Agentic Learning Ecosystemcited by 30
- 2026Auditable Agentscited by 15
- 2025Policy-as-Prompt: Turning AI Governance Rules into Guardrails for AI Agentscited by 14
- 2026Bonded Recourse for Smart-Contract Settlement of Compensable Agent Side Effectscited by 1
Built on by
Not cited yet.
Similar, not cited
Close in wording, no citation either way
- 2025Insured Agents: A Decentralized Trust Insurance Mechanism for Agentic Economy
- 2025VET Your Agent: Towards Host-Independent Autonomy via Verifiable Execution Traces
- 2026Engineering Reliable Commit Gates for Agentic AI: Cost-Aware Verification Portfolios under Common-Mode Data Failures
- 2026PACE: Policy-Attested Contract Execution for Safe AI Agents in Decentralized Finance
- 2026Proof-Gated Signing: Solver-Checked Transaction Guards that Hold Under State Drift for Onchain AI Agents
- 2025Verifiability-First Agents: Provable Observability and Lightweight Audit Agents for Controlling Autonomous LLM Systems
- 2025VeriGuard: Enhancing LLM Agent Safety via Verified Code Generation
- 2026Agentic Commerce Bench: Measuring Fraud Detection for Agents That Spend Money