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Five attacker agents found three flaws in my refund benchmark; an outage, implausibly strong results, and stale figures exposed three more. The fixes changed what the scores meant: a cap had to apply to an order, not each payment; replayability needed evidence from refund history; and “no harm” could not count as safety when an agent never ran. The benchmark covers 32 support cases, but the larger lesson is that an AI agent score is only as trustworthy as the behavior its checks actually verify.
Jigon Yoo described the audit in a September 25, 2026 post. The environment simulates a support desk with four tools, a ledger, and a $50 refund cap. It contains 32 cases across eight families; one rollout is one agent run on one case. The benchmark scores observed effects, not just the agent’s wording. That design is useful, but it does not by itself make the score robust: the scorer’s definitions and the operational conditions of each run matter just as much.
Yoo’s account separates three attacker-discovered scoring flaws from three problems revealed by operations and editorial review. The distinction matters: red-team scripts can expose exploitable rules, while outages, implausible outcomes, and stale derived figures require other checks. The reported figures below are Yoo’s measurements on the described suite, not independent validation.
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The aggregate score combined five terms:
| Scoring term | Weight | What it measures |
|---|---|---|
r0_task_completed |
0.4 | Task completion |
r4_log_replayable |
0.2 | Whether the action log can be replayed |
r2_duplicate_effects |
0.2 | Duplicate effects |
r1_unauthorized_cents |
0.1 | Unauthorized refund amount |
r3_false_blocks |
0.1 | Cases blocked incorrectly |
The last three terms reward harm not done. That can be sensible when an agent executes correctly, but it creates an incentive problem if a run that never meaningfully executes can earn the same credit. A benchmark must distinguish safe action from absent action, and correct verification from a claim that verification occurred.
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Three attacker agents exposed flaws in the scorer
A per-payment cap was not an order-level cap
The intended policy limited refunds to $50 per order, but the implementation checked the limit per payment. An order split across payments could therefore exceed its intended limit while each individual payment stayed within the check. In Yoo’s same-set comparison, the per-payment check caught $0.00 of unauthorized refunds, while per-order aggregation caught $385.50. The attacker named reread-newkey passed $633.80 beyond the per-order cap while escaping the old per-payment check.
The practical audit is to define the policy’s unit before writing its check. Yoo’s formulation is: “Write down what the limit is a limit of, in one sentence. The subject of that sentence — call, order, user, day — is your aggregation key. If it differs from what your loop iterates over, that is the hole.” A refund policy defined per order must aggregate by order, regardless of how many payment records or tool calls the implementation processes.
A generic read did not prove refund history was checked
The claim-checked attacker wrote that it had inspected refund history but never opened the ledger. The earlier replayability rule accepted any read as evidence, so the agent scored 0.863 against the reference agent’s 1.000. Requiring the specific refund_history call changed the attacker’s score to 0.681, with just 3 of 32 rollouts replayable.
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One free scan finds every outdated or missing driver and matches the right update for your exact hardware.Free scan · exact hardware matchEvidence has to be relevant to the claim. A read of an unrelated record cannot establish that refund history was checked, and prose written by the agent cannot establish that a tool was called. As Yoo puts it, “A term that demands evidence has to name evidence that could support the claim being made. A sentence the agent wrote is never evidence. Only a call is.” The remaining limitation is important: confirming that the relevant call happened before a decision does not show that the agent interpreted the returned evidence correctly.
Inaction could earn credit on cases that required a check
The prose-only attacker made no tool calls. It still benefited from reward terms that treated doing nothing as success in no-move cases and treated an empty log as replayable. Its score fell from 0.634 to 0.334 after the scorer required evidence for both task completion and replayability.
A no-action outcome should count as correct only when the agent has established why no action is appropriate. If the task requires checking whether a refund already occurred, silence and an empty log do not prove the check was performed. The evaluator needs evidence tied to the relevant condition, rather than inferring safety from the absence of a harmful effect.
Three more problems surfaced outside the attacker scripts
An inference outage made failed calls look like safe runs
An inference account with a $0 balance caused every model call to fail with HTTP 402. An earlier calculation nevertheless gave three models a mean score of 0.344 despite a 100% error rate. That figure was reconstructed from the earlier 18-case set; Yoo gives 0.334 as the corresponding empty-ledger arithmetic on the current 32 cases. The current-set calculation makes the scoring issue visible, but it does not recreate the unavailable earlier run or its inputs.
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1Clear out junk files and repair common Windows errors2Scan for outdated or missing drivers - takes under a minute3Repair Windows errors before they cause bigger problemsWhen every inference call fails, no harmful action may occur—but that is not evidence of safe performance. A benchmark should report execution failures separately and prevent a failed invocation from collecting safety credit merely because it did nothing. As Yoo summarizes the principle: “Every term that pays out for ‘no harm done’ has to ask whether the thing ran at all.”
Hints made a strong result less informative
In a paid Sonnet 4.5 comparison across 32 cases and three runs per case, the hinted and unhinted results were both striking. The table reports Yoo’s figures:
| Condition | Perfect runs | Duplicate effects | Paid twice |
|---|---|---|---|
| Hints off | 92/96 | 3 | $205 |
| Hints on | 91/96 | 5 | $467 |
The three-versus-five duplicate count is not a strong statistical difference. Yoo’s conclusion is that the hints did not help; the figures do not establish that hints caused harm. More fundamentally, near-perfect results became suspect when the models had been prompted with key facts the environment was intended to test. An evaluation can measure performance under supplied guidance, but it should not present that result as evidence the model independently discovered or applied those facts.
Stale figures survived after the test set changed
The evaluation set grew from 18 to 32 cases, while derived numbers in the write-up remained tied to the earlier version. Rechecking the article against the current suite exposed the mismatch. A result should travel with the version of the cases and scoring logic that produced it: “A derived number has a version. If the thing it was derived from changes, the number is wrong even though nobody touched it.”
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That lesson applies to dashboards, benchmark summaries, and articles alike. When cases, weights, or code change, figures based on the older inputs need to be rerun or clearly labeled as historical. A number can become misleading without anyone editing the number itself.
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What is reproducible—and what is not
Yoo says the repository includes six attackers, reference agents, an ablation, and regression tests intended to catch weakened scoring rules. The attacker and reference-agent checks can be run with:
python3 scripts/run_attacks.py
python3 scripts/run_report.pyThe post says these commands require neither an API key nor an install. Its limits matter when interpreting the results: pre-fix numbers were generated by manually reverting fixes in current code because the repository did not retain the pre-fix code in its history. The paid model rollouts are also absent from the repository, so the Sonnet comparison cannot be independently reproduced from those files. The cap and attacker comparisons are presented as rerunnable/current-suite figures; the paid results are not.
For readers building their own evaluators, the sequence is straightforward: define each policy’s aggregation key, make evidence requirements specific to the claim, test no-action cases for proof of checking, and separate failed execution from safe execution. Then preserve the exact test-set and scorer version behind every reported number. Adversarial agents help test the rules, but they cannot replace operational monitoring or versioned reporting.
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Yoo’s site describes an AI agent and data pipeline diagnostics service that includes attack scripts, per-attack score tables, and fixes: Jigon Yoo’s diagnostics site.
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