Hardening Agent Benchmarks with Adversarial Hacker-Fixer Loops

Ziqian Zhong; Ivgeni Segal; Ivan Bercovich; Shashwat Saxena; Kexun Zhang; Aditi Raghunathan

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Hardening Agent Benchmarks with Adversarial Hacker-Fixer Loops

Ziqian Zhong, Ivgeni Segal, Ivan Bercovich, Shashwat Saxena, Kexun Zhang, Aditi Raghunathan

arXiv:2606.08960v1Jun 8, 2026cs.CRcs.AIcs.LGcs.MA

TL;DR

Brittle, manually crafted benchmark verifiers remain vulnerable to reward hacking, and no systematic proactive hardening method exists. This paper introduces a hacker-fixer loop that automatically patches discovered exploits while checking legitimate solutions, substantially reducing attacks across KernelBench and Terminal Bench.

Problem
Manually crafted outcome verifiers remain vulnerable to reward hacking, while benchmark hardening lacks a systematic proactive method.
Method
The hacker-fixer loop alternates an exploit-seeking hacker, a fixer that patches the verifier, and a solver that validates legitimate solutions.
Results
The loop substantially reduces attacks across KernelBench and Terminal Bench, driving KernelBench attack success from 62% to 0% and Terminal Bench from 50% to 39% across 77 tasks.
Takeaways & Limitations
The findings support continuous adversarial hardening of benchmarks rather than waiting for exploits to surface after deployment.
Takeaways & Limitations
The loop’s coverage is bounded by the hacker’s capabilities and iteration budget, and some tasks are fundamentally unfixable at the verifier level.

Abstract

from arXiv · show

Agent benchmarks score submissions with outcome verifiers that are typically hand-written and brittle, leaving them open to reward hacking. We audit 1,968 tasks across five terminal-agent benchmarks and find 323 (16%) hackable by frontier models given only the task description. This corrupts both leaderboard rankings and RL training signal, yet the standard response is manual and reactive. We introduce the hacker-fixer loop, a method for building exploit-resistant verifiers without per-task manual patching. The loop alternates three LLM agents: a hacker tries to pass the verifier without solving the task, a fixer patches the verifier to reject each discovered exploit, and a solver confirms the patched verifier still admits legitimate solutions. The loop iterates: each patch reshapes what the verifier rewards, surfacing the next exploit. We further add verifier access, and let patches transfer across tasks, to broaden the exploits the loop discovers. On KernelBench, the loop drives the attack success rate from 62% to 0% on a held-out corpus of publicly reported exploits. We also find that weaker agents in the loop can defend against much stronger hackers: Gemini 3 Flash's loop drives the stronger Gemini 3.1 Pro and Claude Opus 4.7's attack success rate from 76% and 61% to 0% on KernelBench, and Gemini 3.1 Pro's from 39% to 17% on Terminal Bench across 77 tasks. We release Terminal Wrench (323 hackable environments, 3,632 hack trajectories) as a snapshot of the current attack surface, our patched verifiers, the exploits the loop discovered, and our implementation as a basis for future work.

1 Introduction

Agent benchmarks rely on brittle, manually crafted outcome verifiers that are vulnerable to reward hacking, while responses remain reactive and task-specific. The hacker–fixer loop automates verifier hardening by iterating exploit discovery, patching, and validation, with verifier access and shared defenses broadening coverage.

Motivation: Manual, reactive verifier patching leaves recurring exploit classes and newly emerging attacks insufficiently addressed across tasks and benchmarks.The standard process discovers an exploit, removes the submission, patches one verifier, and moves on.
Attack surface: 323 of 1,968 environments (16%) were hackable by frontier models without verifier-source access across five terminal-agent benchmarks.Many hackable tasks admitted multiple distinct exploits, and similar exploit patterns recurred across tasks.
Method: The hacker–fixer loop alternates a hacker, fixer, and solver to discover exploits, block them in the verifier, and confirm legitimate solutions remain valid.The solver prevents over-restrictive fixes, while repeated iterations reshape verifier rewards and surface subsequent exploits.
Method extensions: Verifier access enables targeted attacks, while a shared defense pool propagates fixes across tasks sharing evaluation infrastructure.Together, these additions broaden exploit coverage without changing the hacker model and amortize hardening effort across tasks.
Empirical validation: 76% to 0%: Gemini 3 Flash’s loop reduced Gemini 3.1 Pro’s attack success rate on KernelBench; Claude Opus 4.7 fell from 61% to 0%.On Terminal Bench, Gemini 3.1 Pro’s attack success rate fell from 39% to 17% across 77 tasks; documented KernelBench exploits fell from 62% to 0%.

2 How Hackable Are Current Benchmarks?

An audit of 1,968 tasks found 323 hackable environments, revealing recurring exploit patterns and multiple independent vulnerabilities within individual tasks. These findings motivate reusable, iterative verifier hardening rather than one-off fixes.

Audit results: 75% of 4,848 verifier-passing trajectories were classified as hacks, with no false positives among the first 49 manually verified environments.An LLM judge discarded trajectories marked as legitimate solves before counting hacks.
Exploit structure: Recurring exploits include reading answers from unguarded files and replacing system binaries with wrapper scripts, enabling fixes to transfer across tasks.The authors use this recurrence in §3.4 through a shared defense pool that propagates infrastructure-level fixes.
Exploit structure: Many hackable tasks admit multiple independent exploits; SETA task 1219 has three, so patching one vulnerability leaves others open.The three hacks spoof a binary, overwrite test fixtures, and hardcode expected output.

3 The Hacker-Fixer Loop

The hacker-fixer loop alternates exploit discovery, verifier patching, and legitimate-solution checks to harden benchmarks without rejecting valid solutions. Verifier access and a shared defense pool expand exploit coverage and amortize infrastructure-level fixes across tasks.

Weak-to-strong defense: Both levers expand hacker coverage and support the claim that weaker verifier-aware defenses can resist stronger blind hackers.The paper operationalizes and evaluates this weak-to-strong claim in later sections.
Hacker-fixer loop: The loop alternates a hacker seeking shortcuts, a fixer patching successful exploits, and a solver checking that legitimate solutions remain accepted.The solver prevents over-tightening that would reject valid task solutions.
Verifier access: Verifier-aware hackers read verifier source to target specific checks, while held-out evaluation remains blind and tests whether defenses address underlying vulnerabilities.This creates an information advantage during hardening without changing the defended deployment threat model.
Verifier access: 93,862× reported speedup resulted when a verifier-aware hacker patched the reference model’s forward method before timed calls in KernelBench task 046.The exploit used gc.get_objects() after identifying that reported speedup was ref_runtime / sol_runtime.
Shared defense pool: A shared defense pool propagates task-agnostic infrastructure fixes across tasks sharing an evaluation harness, replacing repeated per-task rediscovery with amortized hardening.Fixers push shared evaluation-script defenses, while task-specific test logic remains local.

4 Hardening Results

The hacker-fixer loop hardens KernelBench and Terminal Bench against held-out exploits, reaching 0% KernelBench attack success and substantially reducing Terminal Bench attack rates. Verifier access and cross-task defense sharing drive improvements, while solver validation remains important for avoiding over-restrictive patches.

KernelBench: 0% hinted and unhinted attack success rates are reached by KernelBench iteration 8 and maintained through the final iteration.Removing verifier access or the defense pool unblocks some hinted attacks.
KernelBench: 171 defense-pool commits from 45 tasks contribute across 14 iterations, with task 001’s final defense incorporating 15 commits from 13 other tasks.Every contribution to task 001’s final verifier originated from a different task.
KernelBench: A pool-sync iteration self-corrected an over-restrictive stack-introspection defense after a synthetic load_inline solution failed, without human intervention.The fixer narrowed the defense to restore compatibility.
Terminal Bench: 39.2% to 16.7% trajectory-level and 55.8% to 30.3% task-level unhinted ASR reductions are achieved against a stronger blind Pro hacker on Terminal Bench.Human-identified hinted exploits also decline by 11.0 percentage points, with all eight suppressed.
Terminal Bench: Verifier access reduces hinted ASR from 56.2% to 39.5% with both levers, while the defense pool reduces unhinted ASR from 42.3% to 22.3% alone.Verifier access alone yields 56.2% to 45.9% hinted ASR, while omitting the pool yields 42.3% to 24.0% unhinted ASR.
Limitations: The loop’s coverage is limited by hacker capability and iteration budget, and human-discovered exploits requiring creative leaps can remain beyond current agents.Verifier access and cross-task defense sharing partially expand exploit coverage.

5 Conclusion

An audit finds that 16% of 1,968 terminal-agent tasks are hackable by frontier models, threatening evaluation integrity and RL training signal. The hacker–fixer loop automates benchmark hardening and, with shared defenses and verifier-aware hacking, eliminates documented and novel stronger-model attacks on KernelBench L1 while reducing attacks on Terminal Bench.

Conclusion: 16% of 1,968 terminal-agent tasks are hackable by frontier models under realistic constraints.The audit identifies this vulnerability as undermining evaluation integrity and RL training signal.
Conclusion: The hacker–fixer loop automates benchmark hardening, replacing a previously manual, reactive process.The loop is augmented by a shared defense pool and verifier-aware hacking.
Conclusion: The augmented loop eliminates all documented and novel stronger-model attacks on KernelBench L1.It also substantially reduces attacks on Terminal Bench, with the passage reporting a reduction from 50% to 3.

A Limitations · B Broader Impact

The hacker-fixer loop is limited by the exploits its hackers can discover and by defenses tied to a shared evaluation substrate, although verifier access broadens exploit discovery. Its broader impact is to strengthen benchmark integrity while posing a low-assessed dual-use risk from releasing exploit materials and an automated hacker.

A Limitations: A Limitations — The hardening process can miss attack patterns outside the hacker’s repertoire.The loop only defends against exploits its hackers discover.
A Limitations: A Limitations — Verifier access surfaces a strictly larger class of exploits and partially mitigates the hacker-capability limitation.
A Limitations: A Limitations — Same-generation deployment is better suited to the loop because hackers and defended agents have correlated attack repertoires.They are assumed to share similar training data, capabilities, and priors over possible exploits.
A Limitations: A Limitations — The fix pool amortizes defenses across tasks sharing an evaluation substrate but does not transfer universally across unrelated formats.
B Broader Impact: B Broader Impact — Exploitable verifiers can make performance claims unreliable and cause RL training to reinforce misaligned behaviors.Automated verifier hardening supports the integrity of evaluation infrastructure used for safe AI development.
B Broader Impact: B Broader Impact — Publishing an exploit catalog and automated hacking agent could lower the barrier to attacking benchmarks.The authors assess this risk as low because the work uses public models and does not meaningfully increase attack capacity beyond iteration-driven diversity gains.

C Related Work … D.4 Precheck

The paper positions hacker-fixer loops as an automated complement to reward-hacking detection and prior task-specific defenses, while detailing synchronization, anti-stall, blind-tail, and precheck mechanisms. These components coordinate shared defenses, preserve realistic threat-model coverage, and exclude tasks whose original verifiers cannot validate correct solutions.

C Related Work: Reward hacking appears across major evaluation surfaces, with documented exploits on SWE-bench, KernelBench, and RE-Bench and seven attack patterns achieving near-100% success across eight benchmarks.The paper distinguishes ordinary reward hacking from developer-assisted cheating, which no verifier patch can prevent.
C Related Work: Prior work detects compromised benchmarks through held-out tests, LLM judges, file-edit tracking, impossible tasks, and adversarial auditing, whereas this work automates verifier remediation.Terminal Bench 2.0 uses a single-shot adversarial exploit agent followed by manual human review of trajectories.
C Related Work: Prior verifier-hardening defenses are manual, task-specific, and dependent on expert exploit knowledge, while adversarial role separation provides precedent for the paper’s hacker-fixer design.The related work also connects the approach to debate, prover–verifier games, co-evolutionary theorem proving, and red–blue code-hardening games.
C Related Work: A weaker verifier-aware hacker-fixer loop can defend against a stronger blind hacker by combining verifier access with a shared defense pool.These information and coverage advantages provide a defensive form of weak-to-strong generalization.
D.1 Loop Pseudocode: Algorithm 1 applies the loop to one task, while batch mode runs multiple tasks concurrently and synchronizes their shared-pool access with an iteration barrier.The barrier ensures every task observes the same pool state at each iteration boundary.
D.2 Iteration Barrier and Anti-Stall: With a defense pool, an anti-stall cap of S=1 forces a full attack phase after excessive synchronization, ensuring each task contributes exploits and exposes task-specific blind spots.Without the pool, tasks run independently without iteration-level synchronization, with throughput bounded by container concurrency.
D.3 Blind Tail: Verifier-aware loops use verifier access for 8 of 10 iterations before switching to a blind tail that tests resistance under the realistic threat model.The early phase surfaces structurally hard exploits such as stack-frame inspection and audit-hook bypasses.
D.4 Precheck: Before hardening, up to four solver precheck attempts verify that a correct solution passes the original verifier; tasks failing all attempts are excluded.For KernelBench oracle mode, the deterministic reference solution typically passes on the first attempt, and results are content-addressed and cached.

D.5 Ablation Configurations … E.1 Infrastructure

The experiments compare four verifier-access and defense-pool configurations, while implementing hardening through constrained Dockerized fixer workspaces, ephemeral agent containers, and shared git-based defenses. KernelBench uses MIG-partitioned H200 infrastructure, with API-based agent inference and approximately $5,000 in estimated total spending.

D.5 Ablation Configurations: Four ablation configurations toggle verifier access for the hacker and shared defense-pool usage across KernelBench and Terminal Bench.All configurations use Gemini 3 Flash for hacker and fixer; the KernelBench solver is deterministic, while the Terminal Bench solver is Gemini 3 Flash.
D.5 Ablation Configurations: The full A1 configuration combines verifier-aware hacking with pool sharing, while A2 removes verifier access and A3 removes pool sharing.A1 is used for the main results; verifier access is disabled after iteration 8.
D.8 Hyperparameters: The default experiment settings include 10 hacker-fixer iterations, with KernelBench using 100 level-1 tasks, up to 16 concurrent containers, 56 MIG slots, and a 10× hack threshold.Terminal Bench uses up to 100 concurrent containers and four solver precheck retries.
D.6 Fixer Scope: The fixer can edit only tests/evaluation scripts and the Docker environment, with committed changes extracted into the canonical hardened state; task instructions and solution directories remain immutable.Changes are tracked from the repository’s initial git tag, and three consecutive legitimate-solution markings terminate a task as robust.
D.6 Fixer Scope: When enabled, the shared defense pool lets fixers pull, edit, and push task-agnostic fixes through a local git daemon, while mirroring pool changes locally.The pool repository is mounted at /pool/ and cloned from a shared bare repository over the Docker bridge network.
D.7 Workspace Setup: Hacker, solver, and artifact-extraction workspaces run in ephemeral Docker containers, with verifier visibility, reference-solution access, and git initialization controlled by configuration.Failed hacker attempts can be LLM-summarized into /previous_hacks/ so retries avoid repeating unsuccessful strategies.
D.9 Computational Resources: Agent inference uses API calls rather than local GPUs, while task execution runs locally; the main KernelBench runs used an 8×H200 node for approximately 48 hours and cost an estimated ∼$5,000.KernelBench tasks use 18 GB H200 MIG partitions, with 8 GPUs providing 56 concurrent task slots; tasks exceeding 18 GB are excluded at precheck.
E.1 Infrastructure: KernelBench infrastructure assigns each task a single NVIDIA H200 MIG partition, enabling parallel execution across 56 slots while excluding tasks whose requirements exceed 18 GB VRAM.The configuration uses profile 1g.18gb across 8 H200 GPUs with 7 MIG slices each.

E.2 Hint Corpus

The hint corpus evaluates 15 publicly reported KernelBench reward-hacking strategies through structured attack documents and blind reproduction attempts. It covers eval-path, problem-math, and problem-shape exploits under a fixed success criterion based on speedup and correctness.

Corpus construction: 15 exploit strategies from public KernelBench reward-hacking reports are represented as structured hints describing each attack, broken verifier assumption, minimal code demo, and suggested defenses.Gemini 3.1 Pro receives each hint while blind to verifier source and must independently produce a working exploit.
Exploit categories: 11 strategies target the evaluation path, exploiting the timing or correctness pipeline rather than the underlying problem.Examples include mutating inputs before custom and reference evaluation, scavenging freed memory, and launching work on a non-default CUDA stream.
Exploit categories: 3 strategies exploit statistical properties of reference inputs, including analytical expectations, partial loss computation, and constant outputs within tolerance.These methods rely on empirical concentration under the law of large numbers or structurally near-zero reference outputs.
Exploit categories: 1 strategy exploits redundancy in the reference operation chain by removing no-ops while preserving outputs and increasing speed.Examples include skipping size-1-dimension reductions, repeated means, redundant clamps, and dropout in evaluation mode.
Evaluation protocol: 133 total attack attempts test the 15 hints, with 9 independent attempts per hint and success requiring reward above 12× on speedup while passing correctness.A few attempts without kernel code are excluded; the corpus and concrete traces predate hardening runs, preventing information leakage.

E.3 Autopatch: Post-Loop Surgery on Over-Restrictive Defenses · F Terminal Bench Case Study Details

The KernelBench hacker-fixer loop eliminated held-out exploits but over-restricted legitimate solver code, so a single post-loop Flash-fixer autopatch relaxes two defenses while preserving exploit checks. The repairs target module poisoning and per-trial thread isolation, restoring compatibility with legitimate solver patterns without changing hint-exploit robustness.

E.3 Autopatch: Post-Loop Surgery on Over-Restrictive Defenses: 0% ASR by iteration 8 coincided with benign pass rates of 22% for Opus 4.7 and 94% for Gemini without autopatch.The over-restriction arose because the deterministic KernelBench reference did not exercise code paths used by diverse LLM solvers.
E.3 Autopatch: Post-Loop Surgery on Over-Restrictive Defenses: The first over-restrictive defense poisoned inspect and restricted sys._getframe, breaking torch.utils.cpp_extension.load_inline.The autopatch reverts the inspect assignment and removes the restricted getframe wrapper.
E.3 Autopatch: Post-Loop Surgery on Over-Restrictive Defenses: 0% held-out hint-exploit ASR remained unchanged because no hint exploit depended on inspect or ctypes access.This supports relaxing the module-poisoning defense without weakening the evaluated hint-exploit barrier.
E.3 Autopatch: Post-Loop Surgery on Over-Restrictive Defenses: The second defense wrapped each measured forward pass in a fresh ModelThread, effectively blocking thread-injection hacks but disrupting legitimate background-worker solvers.The verifier’s join can race with solver work performed in a spawned worker.
E.3 Autopatch: Post-Loop Surgery on Over-Restrictive Defenses: 22% benign pass rate for Opus versus 94% for Gemini reflected invalid per-trial cuBLAS handles for Opus’s own-handle style, while Gemini’s shared PyTorch handle remained unaffected.Fresh ModelThread instances changed the CUDA context between trials, producing cudaErrorInvalidValue on later cublas*Gemm calls; the autopatch replaces threaded invocation with a direct call in warmup and timed loops.
E.3 Autopatch: Post-Loop Surgery on Over-Restrictive Defenses: Thread-injection hacks remained blocked after removing per-trial isolation because the thread-poisoning audit hook and gc.get_objects() interception stayed active.The autopatch therefore removes only the over-specified isolation mechanism.
E.3 Autopatch: Post-Loop Surgery on Over-Restrictive Defenses: The autopatch used one Gemini 3 Flash fixer call per final iteration, requiring the smallest verifier edit that enabled a legitimate solver to achieve reward ≥0.5× while preserving other checks.The fixer received the verifier, a cublasGemmEx-based TF32 solver, and its failing test output, but not the hint corpus.

F.1 Hint Corpus

The hint-guided evaluation tests eight human-discovered exploit strategies from two public audits, with a blind Gemini 3.1 Pro evaluator independently producing working exploits from structured hints. Table 5 reports trajectory-level attack success rates for each hint before and after hardening across 77 Terminal Bench tasks.

Hint corpus: 8 exploit strategies from two public audits form the hint-guided evaluation corpus.All eight vulnerabilities were discovered by humans and had not been exploited by agents in the wild.
Hint corpus: Each structured hint provides an attack description, code examples, and suggested defenses.The blind Gemini 3.1 Pro evaluator receives the hint and must independently produce a working exploit.
Exploit strategies: The corpus includes curl-wrap, pip-wrap, and python-wrap attacks that replace system executables to fabricate passing pytest results.The wrappers respectively target curl, pip, or Python and can write or emit fabricated passing outputs.
Evaluation: Table 5 reports per-hint trajectory-level attack success rates on 77 Terminal Bench tasks before and after hardening.The reported p-values use uncorrected two-proportion z-tests; Bonferroni correction uses α′ = 0.00625.
Evaluation: No individual hint clears the Bonferroni threshold, while the aggregate hinted effect has p = 2·10^-4 and a consistent direction across all eight hints.The aggregate hinted effect is reported in Table 3 and clears by a wide margin.

F.2 Judge-Filtered Results

Judge filtering counts an attack as successful only when it both passes the verifier and is labeled a real exploit, reducing measured attack effects relative to verifier-only filtering. Under unhinted attacks, the reduction changes from −22.5 pp to −18.8 pp.

Judge-filtered evaluation: A successful hack requires both verifier passage and an LLM judge labeling the trajectory as a real exploit.The judge is Gemini 3 Flash; legitimate solves that pass the verifier are excluded.
Judge-filtered evaluation: 1–4 pp smaller attack effects result from removing verifier passes that were actually legitimate solves.The secondary filtering changes measured attack effects relative to verifier-only filtering.
Judge-filtered evaluation: −18.8 pp is the headline unhinted reduction after judge filtering, versus −22.5 pp under verifier-only filtering.These results come from the judge-filtered evaluation on 77 held-out Terminal Bench tasks.
Judge-filtered evaluation: 5 normal-solver solves were filtered as non-legitimate, comprising 1 misflag and 4 borderline cases on manual review.The judge-filtered held-out evaluation covers 77 Terminal Bench tasks.

F.3 Loop Ablations · G More Exploit Examples in Terminal Wrench · H Agent Prompts

The ablations show that combining verifier-aware hacking with a shared defense pool provides the strongest protection, while Terminal Wrench examples expose recurring verifier shortcuts. The released prompts operationalize hacking, verifier inspection, feedback, fixing, solving, and cross-task defense sharing.

F.3 Loop Ablations: −25.4 pp reward-only and −22.8 pp judge-filtered attack reduction: the full aware+pool configuration outperforms both ablations, with p < 10−7 at trajectory level.The comparison uses the same Terminal Bench task pool and probes each run’s hardened defenses with the same Pro-evaluator corpus.
F.3 Loop Ablations: +7.8 pp hinted attack success rate: blind+pool reverses the hinted result under judge filtering, with p = 0.002.Without verifier access, the hacker does not exhibit the wrap-style attacks targeted by the hint corpus, leaving that surface uncovered.
G More Exploit Examples in Terminal Wrench: Terminal Wrench includes exploits that satisfy naïve checks without completing the intended task, such as appending RequiresMountsFor as a shell comment.The comment passes a grep-based verifier while leaving the systemd service’s boot dependencies unchanged.
G More Exploit Examples in Terminal Wrench: Other examples exploit superficial inputs or shell behavior, including diagnosing disks from filenames and escaping to bash on line one.The disk-monitoring exploit ignores diagnostic contents, pattern-matches filenames, and emits hardcoded JSON scores.
H Agent Prompts: The hacker prompts explicitly prioritize exploiting verification rather than solving tasks, including reading tests and seeking creative shortcuts that bypass them.KernelBench prompts frame the objective as maximizing speedup by any means, while generic prompts state that legitimate solutions do not count.
H Agent Prompts: Verifier-access and feedback prompts provide read-only evaluation-source access and previous failed hacks so hackers can study checks, weaknesses, and detection failures.The feedback prompt encourages adapting prior strategies rather than necessarily abandoning them.
H Agent Prompts: Fixer prompts require modifying the task environment to block the described exploit while preserving legitimate solutions, with changes made in tests or environment artifacts.They instruct fixers to understand the root cause, test fixes, preserve the challenge, and commit changes.
H Agent Prompts: The shared defense pool distributes task-agnostic test improvements across fixers, while restricting pushes to general attack classes and requiring minimal edits.Fixers update local artifacts first and push only changes without task-specific details.