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RecentarXiv preprint

Auditing Agent Harness Safety

An agent can hand back the right answer after reading records it should not, leaking data between agents, or calling tools it was never given. This benchmark audits the whole run, not the reply.

Breakdown by FDE Interviews · 13 min read · Updated

About the original · 2026
AUTHORS
Liu, Guo, Liu, Yang, et al.
FROM
UC Santa Barbara, UC Berkeley, UW–Madison, Stanford, Microsoft Research
PUBLISHED
May 2026 · arXiv preprint
CITED BY
11Semantic Scholar · Oct 2026
IN THIS BREAKDOWN · 10 SECTIONS

You can read 16% of this breakdown.

01

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.

THE IDEA, VISUALLY
A RIGHT ANSWER CAN HIDE A BAD RUN planner → retriever: task allowed retriever reads customer:1042 allowed retriever reads customer:1043 out of scope retriever → refunder: id, amount, card, address field leak refunder refunds order A77 allowed refunder calls crm.export tool not granted final answer: refund issued, correct Output-only scoring calls this run a success. The trajectory audit finds three violations, from logs the agents cannot see or edit. MORE COMPLETION, LESS ADHERENCE 10 harness configurations (Table 2); correlation −0.73 0.5 0.6 0.7 0.8 0.3 0.5 0.7 task completion (TCR) safety adherence (SAR) Gemini 3.1 Pro Opus 4.6 Opus 4.6 · Claude Code GPT-5.4 · Codex OpenClaw Claude Code Codex
A run can end in the correct answer and still break three rules on the way. Across ten harness configurations, the ones that completed more of each task kept to their boundaries less.
Research notes: the problem, approach and results
0.41

best overall harness safety score of ten configurations (Gemini 3.1 Pro under OpenClaw), out of 1

−0.73

correlation between task completion and safety adherence across the ten configurations

0.85 → 0.63

resource-access adherence, single agent versus multi-agent teams

THE PROBLEM

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.

THE IDEA

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.

THE RESULT

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.

02

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.

THE FULL BREAKDOWN

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  • ◇1 architecture diagram
  • </>1 runnable code experiment
  • ▦2 results tables
  • ?5 interview questions with answers
  1. 03Key takeaways

    The policy model, the three audit layers, the findings by configuration, and the misreadings

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  2. 04How it works

    The formal policy model, the audit pipeline, the scores and how the benchmark was built

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  3. 05The numbers that matter

    Channel-level results, single versus multi-agent, and two inconsistencies in the paper

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  4. 06Try it yourself

    Audit a multi-agent trajectory against an explicit policy, then stress-test the paper's ranking

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  5. 07Where it breaks

    The limits of a synthetic, policy-defined benchmark and its scoring choices

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  6. 08Using it on a deployment

    Building a trajectory audit for a customer's agent before go-live

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  7. 09Interview angles

    Five questions this paper prepares you for, with answer hints

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