Holding Agents Accountable with Verifiable Agent Insurance
cs.CR, cs.AI
Submitted: 2026-10-13
Updated: 2026-10-13
Comments: 21 pages, 1 figure, 7 tables
Code: https://github.com/ChengHua926/vai-artifact2https:
Project page: https://openai.github.io/openaiagents-python/ref/lifecycle
License: http://creativecommons.org/licenses/by/4.0/
The gist: While AI agents are useful for many tasks, they regularly misbehave in destructive ways, such as deleting important data.
Abstract
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.
Sources
- Bonded Recourse for Smart-Contract Settlement of Compensable Agent Side Effects
- Defeating Prompt Injections by Design
- The Shift to Agentic AI: Evidence from Codex
- Policy-as-Prompt: Turning AI Governance Rules into Guardrails for AI Agents
- ClawsBench: Evaluating Capability and Safety of LLM Productivity Agents in Simulated Workspaces
- Auditable Agents
- Progent: Securing AI Agents with Privilege Control
- AgentSpec: Customizable Runtime Enforcement for Safe and Reliable LLM Agents
- Let It Flow: Agentic Crafting on Rock and Roll, Building the ROME Model within an Open Agentic Learning Ecosystem
- $\tau$-bench: A Benchmark for Tool-Agent-User Interaction in Real-World Domains
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