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The paper in one minute
Most agent evaluations check the answer. If the refund was issued correctly, the run passes. But an agent working through a harness can reach that answer after reading a neighbouring customer's record, forwarding card digits to a teammate agent that needed only the order number, or calling an export tool its role was never granted. An answer-only check calls that run a success.
Chengzhi Liu, Xin Eric Wang and colleagues at UC Santa Barbara, with co-authors at Berkeley, Wisconsin, Stanford and Microsoft Research, propose HarnessAudit. Each task comes with an explicit policy: which role may call which tools, touch which resources, and pass which information to whom. The harness runs normally while the framework records every tool call, resource access and inter-agent message, plus snapshots of the environment, through logs the agents cannot see or influence. After the run, hidden audit rules are applied to that record.
The audit has three layers. Boundary compliance checks every action against the tool, resource and information-flow rules. Execution fidelity checks that the run reached its goal through valid steps, using task milestones rather than only the final answer. System stability repeats tasks with indirect prompt injections, ambiguous goals and tool errors. The benchmark, HarnessAudit-Bench, has 210 tasks across finance, e-commerce, healthcare, office work, social, daily life, legal and software engineering, run by single agents and by teams of four or five role agents.
Research notes: the problem, approach and results
best overall harness safety score of ten configurations (Gemini 3.1 Pro under OpenClaw), out of 1
correlation between task completion and safety adherence across the ten configurations
resource-access adherence, single agent versus multi-agent teams
Agent safety benchmarks mostly score the final answer or end state. Many violations happen mid-run: reading the wrong record, sharing a field with the wrong agent, calling a tool outside a role. Those runs look like successes.
Define the harness's permission and information-flow policies explicitly, log every tool call, resource access and message through channels the agents cannot see, then audit the trajectory for boundary compliance, execution fidelity and stability under stress.
On 210 tasks in eight domains, no configuration scored above 0.41 of 1. Completion and safety pulled in opposite directions. Most violations were correct tools applied to the wrong records, and multi-agent setups leaked sensitive fields between agents.
Why it matters to an FDE
A customer's security or compliance team will not ask whether the agent gave the right answer. They will ask what it accessed, what it passed along, and what record exists. This paper turns those questions into an evaluation you can build.
Write the policy down before you test. For each role or agent: allowed tools, allowed records, and which fields may cross to which other role. Without that, there is nothing to audit against.
Log outside the agent. The paper's audit evidence comes from tool wrappers and backend logs, never the agent's own report of what it did. An agent's summary of its actions is not an audit trail.
Expect the wrong record, not the wrong tool. Agents rarely called obviously inappropriate tools. They called reasonable tools on the wrong objects: an adjacent account, an out-of-scope file. Scope checks on arguments matter more than tool allowlists.
Treat multi-agent designs as a data-sharing risk. Teams leaked sensitive fields to each other far more than they sent messages to the wrong recipient. Field-level rules on what passes between agents are needed.
Do not trade safety away for completion scores. The configurations that finished the most work broke the most rules. Report both numbers to the customer.
Understand the mechanism. Try it on a real problem.
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- ◇1 architecture diagram
- </>1 runnable code experiment
- ▦2 results tables
- ?5 interview questions with answers
- 03Key takeaways
The policy model, the three audit layers, the findings by configuration, and the misreadings
Free account - 04How it works
The formal policy model, the audit pipeline, the scores and how the benchmark was built
Premium - 05The numbers that matter
Channel-level results, single versus multi-agent, and two inconsistencies in the paper
Premium - 06Try it yourself
Audit a multi-agent trajectory against an explicit policy, then stress-test the paper's ranking
Premium - 07Where it breaks
The limits of a synthetic, policy-defined benchmark and its scoring choices
Premium - 08Using it on a deployment
Building a trajectory audit for a customer's agent before go-live
Premium - 09Interview angles
Five questions this paper prepares you for, with answer hints
Premium - 10Read next
Where to go from here on this site
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