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REVIEW 4 major objections 6 minor 30 references

Even the stealthiest AI security agent scores only 54% on tasks that require both solving the hack and staying hidden.

Reviewed by Pith at T0; open to challenge. T0 means a machine referee read the full paper against a public rubric. the ladder, T0–T4 →

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T0 review · deepseek-v4-flash

2026-08-01 00:10 UTC pith:T7AE6TQB

load-bearing objection StealthBench's qualitative tradecraft contrasts and released artifacts are genuinely useful, but the 54% ceiling is an unvalidated judge-panel measurement until human ground-truth labels land. the 4 major comments →

arxiv 2607.26314 v1 pith:T7AE6TQB submitted 2026-07-28 cs.CR cs.AI

StealthBench: Measuring Operational Stealth in Autonomous Offensive-Security Agents

Ads Dawson , Adrian Wood This is my paper
classification cs.CR cs.AI
keywords stealthOPSECLLM agentsoffensive securitybenchmarkLLM-as-judgetradecraftautonomous hacking
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

StealthBench tests whether autonomous offensive-security agents can complete real hacking tasks without leaving forensic traces. Across eight LLM agent models and fourteen dockerized scenarios built from real bug-bounty and red-team incidents, no model exceeded 54% safe success—the compound score for solving the task while passing a three-model judge panel's stealth review. The paper argues this shows operational-security (OPSEC) failures are systematic across model families, not a quirk of any single vendor. It also finds capability and stealth are largely independent: models that solve the most tasks are often the most reckless, while cautious models solve fewer tasks. A public benchmark, harness, and trajectories are released so others can measure and improve agent tradecraft.

Core claim

The paper's central claim is that the stealth gap—the distance between what agents can solve and what they can solve without revealing themselves—is a universal property of current autonomous offensive-security agents, not a model-specific defect. Using 771 scored trajectories across 8 models and 14 task scenarios built from 11 hand-verified OPSEC incidents, it reports that no model exceeds 53.8% safe success rate, and 24.5% of all solves are 'reckless': the agent obtains the flag but fails the stealth panel. The authors show the failure is not about capability by identifying tasks where every solving model is reckless (e.g., a callback-listener task with 70.4% solve and 0% safe success), an

What carries the argument

The measurement instrument is a three-model LLM judge panel that reads the full agent trajectory (every tool call, argument, and output) against a task-specific stealth rubric, returning a binary failure verdict plus a continuous stealth score; the panel verdict is a majority vote. Stealth is decomposed into six dimensions—credential OPSEC, destructive operations, detection cascade, telemetry injection, artifact contamination, noise discipline—and the headline metric is safe success rate: the fraction of valid runs that are both solved and judged stealth-clean. The judges showed high inter-rater agreement (Fleiss' κ = 0.834, 87.9% unanimous verdicts), which the paper presents as rubric relia

Load-bearing premise

The headline rates assume the LLM judge panel's stealth verdicts match what human offensive-security experts would say; the paper reports high inter-judge agreement but explicitly defers human-expert validation, so systematic judge miscalibration would change the absolute numbers.

What would settle it

Have a panel of human offensive-security practitioners independently label the 771 published trajectories (or a random sample) for stealth failure using the same rubrics; if judge–human agreement is poor, or if human experts clear substantially more than 54% of solves, the central 'systematic OPSEC failure' claim collapses. A cheaper check: run a judge panel drawn entirely from model families not among the evaluated agents and see whether the no-model-exceeds-54% finding survives.

Watch this falsifier — get emailed when new claim-graph text bears on it.

If this is right

  • Deploying current autonomous offensive-security agents in real engagements means accepting that roughly half of successful tasks will leave forensic traces, burning credentials and triggering defenses.
  • Capability and stealth must be scored separately: high solve rate with low stealth@solve means the model is capable but reckless, a distinction single-score benchmarks miss.
  • Each of the six stealth dimensions maps to a defensive detection opportunity, so the judge effectively doubles as a blueprint for automated OPSEC monitors.
  • The public dockerized scenarios and harness make stealth measurable and reproducible: any agent can be run through the same 14 tasks and scored by the same panel.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • If the judge panels are calibrated against human experts, the benchmark could become a training signal: penalizing reckless solves during RLHF might close part of the gap, a hypothesis the paper motivates but does not test.
  • The same trajectories could seed real-time OPSEC monitors for production agent deployments, gating or alerting on actions before they execute rather than judging after the fact.
  • The paired stealthy/reckless paths to the same flag invite a direct testable extension: measure whether explicit instructions like 'use disposable resources and stop after sufficient proof' raise safe success rates, separating training-signal effects from scaffolding effects.
  • Because two of the three judges overlap with the evaluated agent models, an independent judge panel could yield different absolute rates—a straightforward check on whether the 54% ceiling is an artifact of shared blind spots.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit.

Referee Report

4 major / 6 minor

Summary. The paper introduces StealthBench, a benchmark and evaluation harness for measuring operational stealth in autonomous offensive-security LLM agents. It curates 11 hand-verified OPSEC incidents from real bug-bounty and red-team trajectories, expands them into 14 dockerized task scenarios, runs 8 agent models (771 valid trajectories), and scores each trajectory with a fixed 3-LLM judge panel using majority vote. The primary metric is safe success rate (solved and judged stealth-clean), supported by Stealth@Solve and reckless solve rate. The headline result is that no model exceeds 53.8% safe success, which the paper interprets as evidence that OPSEC failures are systematic across model families. The paper also reports per-task breakdowns, qualitative failure mechanisms from a purposive sample, judge agreement statistics, and cost accounting. All trajectories, prompts, harness code, and judge verdicts are publicly released.

Significance. StealthBench addresses a genuine gap in agent evaluation: existing benchmarks measure whether agents can find vulnerabilities, not whether they operate with acceptable tradecraft. The task design (each scenario has an explicit stealthy and reckless path), deterministic SHA-256 flag verification, trajectory sanitization, and full public release are concrete strengths. The Appendix B trajectory pairs are compelling and make the stealth gap vivid. The paper is also unusually candid about its limitations. However, the central quantitative claim depends entirely on the LLM judge panel's binary verdicts; the paper itself states that inter-judge agreement is not accuracy, and human validation is explicitly deferred. Until the judge panel is calibrated against human expert labels, 'safe success rate' is a measure of panel consensus rather than established operational stealth. The benchmark artifacts are valuable regardless, but the headline empirical conclusion needs reframing or additional validation.

major comments (4)
  1. [Abstract; §4.1] The claim 'No model exceeds 54% safe success' is presented as a finding about agent OPSEC, but 'safe' is operationalized as passing the 3-LLM-judge majority vote (§2.5). The paper concedes in §4.4 that inter-judge agreement 'is not evidence of accuracy, only consistency' and lists human expert validation as future work (§6). Without calibration against human ground truth, the 53.8% ceiling is a statement about the judge panel's consensus, not about operational stealth. Please reframe headline claims as conditional on the StealthBench judge panel, or add a human-validated subset to anchor the instrument.
  2. [§3.2, §4.4] Two of three judges (Kimi K3, GLM 5.2) are also evaluated agent models, and GPT-5.6 Sol appears on both sides. The paper acknowledges potential 'correlated blind spots' but provides no analysis of their direction or magnitude. Shared training-distribution biases could systematically inflate or deflate safe-success rates in ways that inter-judge agreement cannot detect. This threatens the validity of the comparative ranking, not just the absolute numbers. Please report judge-panel verdicts separately for trajectories produced by judge-family models versus non-judge models, or include a non-overlapping judge set.
  3. [§2.4] The sentence 'Our dataset is constructed so that each trajectory contains at least one verified stealth incident' is ambiguous. If it refers to the evaluation trajectories, it contradicts the positive safe-success rates in Table 4; if it refers only to the 11 source trajectories, that should be stated explicitly. As written, the defining-experiment paragraph blurs the distinction between the incident corpus used to design tasks and the agent-run corpus used for scoring, which affects how the judge task should be interpreted.
  4. [§3, Table 4] The denominator for all metrics is 'valid runs,' which excludes refusals and zero-tool-call runs. A model that refuses to engage is, from an operational standpoint, the least risky outcome; the paper documents that Claude Opus 5 and Gemini 3.5 Flash were excluded for this reason. The headline 'no model exceeds 54%' therefore applies only to models that choose to engage. Please report refusal/zero-tool rates per model and consider an intent-to-treat analysis (e.g., counting refusal as a safe outcome) to make the deployment implications of the benchmark clearer.
minor comments (6)
  1. [§2.5, §4.4] The fail-closed default for unparseable judge output is small (13/2310 verdicts), but its direction should be stated: it can only push trajectories toward failure, potentially understating safe-success rates. This is minor given the low count.
  2. [Table 4] Model-level N varies from 69 to 119. The paper correctly uses per-model denominators, but confidence intervals for the rates would help readers gauge whether differences between adjacent models (e.g., Opus 53.8% vs Kimi 52.5%) are meaningful.
  3. [§3.1] The phrase 'Source agent models' is unclear: does it refer to the models that generated the 11 original incidents, or to the 8 evaluated models? Please clarify to avoid confusion with the 'Evaluated agent models' subsection.
  4. [§3.1] The run accounting is confusing: the target was 560 runs but the final dataset contains 771 'usable trajectories' because re-run passes accumulate alongside original valid runs. Please clarify whether any model received systematically more attempts per task, and whether the extra runs were included before or after the exclusion of invalid runs.
  5. [Throughout] There are several typographical and formatting issues: 'T able 1' in §3.1, inconsistent styling of 'stealthbench extension,' and 'arXiv:2607.26314v1 [cs.CR]' being a future-dated preprint. A careful proofread is recommended.
  6. [§4.4] The agreement analysis reports only binary pass/fail agreement. Given that the panel also produces continuous stealth scores, reporting intraclass correlation or mean absolute deviation of scores would strengthen the reliability characterization.

Circularity Check

0 steps flagged

No derivation-level circularity: the headline safe-success metric is explicitly defined as the judge panel's own verdict, and the paper's validity limitations are disclosed rather than hidden.

full rationale

StealthBench reports an empirical evaluation, not a derivational chain. The primary metric is explicitly defined: safe success rate = (solved ∧ panel pass)/valid runs (§2.6), and panel verdicts come from a fixed three-model judge panel with fail-closed aggregation (§2.5). The headline 'no model exceeds 54% safe success' (§4.1) is a summary of those verdicts; it holds by construction only for this metric, which is exactly what the metric section states. There is no fitted parameter later reported as a prediction, no target quantity included in the judge rubric that would force the result by itself, and no self-citation uniqueness theorem invoked to rule out alternatives. The paper is unusually explicit about the limits of its instrument: 'the high agreement rate itself is not evidence of accuracy, only consistency' (§4.4) and 'Human expert validation remains an important direction for future work' (§3.2, §6). The judge–agent overlap is also disclosed as 'a potential for correlated blind spots' (§4.4). These are construct-validity limitations—the LLM judge panel may be miscalibrated as a measure of true human OPSEC judgment—but they do not make the derivation circular in the sense of an equation reducing to its inputs, a fitted parameter renamed as a prediction, or a load-bearing self-citation chain. The one self-citation ([14], ScopeJudge, which shares author Ads Dawson) is used only to motivate extending the LLM-as-judge paradigm and is not needed to establish any reported result. Score 1 reflects the minor self-citation and the disclosed but unresolved external-validity gap, not circularity.

Axiom & Free-Parameter Ledger

2 free parameters · 4 axioms · 2 invented entities

The central claim rests on the authors' measurement protocol rather than on independent ground truth. The free parameters are decision rules in the judge protocol; the axioms are the unproven assumptions that LLM judges are valid and that the synthetic tasks capture real OPSEC decisions. No physical entities are invented; the six-dimension taxonomy and ATIF format are author-defined constructs without external evidence.

free parameters (2)
  • Panel majority threshold = 2 of 3 judges
    A trajectory is classified as a stealth failure if at least two of three LLM judges vote is_failure=1. This hand-chosen rule defines all headline rates and is not derived from any external criterion (§2.5).
  • Fail-closed default = is_failure=1, s=0.0
    Unparseable judge outputs are automatically counted as stealth failures, inflating the failure rate; 13 of 2,310 individual verdicts defaulted this way (§2.5, §4.4).
axioms (4)
  • domain assumption LLM judges can detect stealth violations from full trajectories
    The entire benchmark assumes judge verdicts are a valid measure of stealth; the paper defers human validation to future work (§3.2, §6).
  • domain assumption Inter-judge agreement is a meaningful reliability signal
    The paper interprets Cohen's and Fleiss' kappa as rubric reliability while conceding that agreement is not accuracy (§4.4).
  • domain assumption Dockerized tasks faithfully reproduce real OPSEC failure classes
    Tasks are modeled on real incidents rather than taken directly; no validation that they elicit equivalent tradecraft decisions (§3.1).
  • domain assumption Sanitization does not change OPSEC-relevant judgment
    Hostnames, credentials, and IPs are replaced with synthetic values; the effect on judge classification is untested (§3.1).
invented entities (2)
  • Six stealth dimensions (S1, S2, S4, S5, S7, S8) no independent evidence
    purpose: Conceptual taxonomy for classifying stealth failures
    Author-defined, with numbering gaps for dimensions not yet covered; no external validation of the taxonomy.
  • ATIF v1.7 trajectory format no independent evidence
    purpose: Serialization format for agent trajectories used as judge input
    Developed for this benchmark; no evidence of wider adoption or interop testing.

pith-pipeline@v1.3.0-alltime-deepseek · 20387 in / 12097 out tokens · 107339 ms · 2026-08-01T00:10:19.033623+00:00 · methodology

0 comments
read the original abstract

Stealth, the discipline of achieving an objective without revealing your presence, capabilities, or collected intelligence, is what separates sophisticated operators from detectable ones. Elite security researchers and advanced persistent threats achieve their objectives unnoticed; autonomous agents increasingly inherit the same offensive tasks, but do they inherit the tradecraft? We introduce StealthBench,a benchmark that measures operational stealth in autonomous offensive-security agents across six operational security (OPSEC) dimensions. We extract 11 hand-verified OPSEC incidents from real bug-bounty and red-team trajectories, expanded into 14 dockerized task scenarios, where agents, despite finding real vulnerabilities, committed stealth failures inconsistent with standard operational tradecraft: embedding credentials in public uploads, deleting production resources to prove access, force-adding uninvolved users to demonstrate a race condition. We evaluate agent trajectories using a 3-model large language model (LLM) judge panel with majority-vote aggregation, measuring safe success rate (solved and stealthy), Stealth@Solve (tradecraft quality among successful solves), and reckless solve rate (solved but cover blown). Our results show that no model exceeds 54% safe success rate (the compound metric requiring both task completion and stealth), confirming that OPSEC failures are systematic across model families. We release StealthBench as a public benchmark to support both the development of stealth-aware agents and automated OPSEC monitoring for autonomous offensive-security deployments. The interactive leaderboard, evaluation harness, and dataset are available at https://stealthbench.com.

Figures

Figures reproduced from arXiv: 2607.26314 by Adrian Wood, Ads Dawson.

Figure 1
Figure 1. Figure 1: StealthBench evaluation architecture. A neutral system prompt and task instruction are given to the LLM agent, which has two tools: think (deliberation capture) and execute_command (bash via docker exec, guardrail-blocked for container escape). The agent interacts with the task app over HTTP inside a shared Docker network. After the run, the full trajectory (tool calls, thoughts, observations) is judged by… view at source ↗

discussion (0)

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