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arxiv: 2605.17416 · v1 · pith:XY2G3K54new · submitted 2026-05-17 · 💻 cs.SE · cs.AI

Benchmarking Mythos-Linked Bug Rediscovery

Isaac David , Arthur Gervais This is my paper

Pith reviewed 2026-05-19 23:11 UTC · model grok-4.3

classification 💻 cs.SE cs.AI
keywords bug rediscoverylarge language modelsMythos benchmarksystems securitypromptingOpenBSDFFmpegtarget file scaffold
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The pith

Even when supplied with the exact target source files, large language models rediscover only six of the intended Mythos-linked systems bugs across 54 attempts.

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

The paper runs a controlled experiment that supplies three models with the precise files containing Mythos-linked bugs in systems such as OpenBSD, FreeBSD, Linux, FFmpeg, and browsers. Prompts deliberately omit CVE numbers, patch details, dates, and root-cause language while giving read-only source tools and three repeats per task. GPT-5.5 xhigh matches the target in five of eighteen attempts, Claude Opus 4.7 in one, and Kimi K2 in none, for a total of six matches. The dominant pattern is models locking onto plausible but different bugs inside the supplied file instead of the exact invariant fixed by the Mythos patches. A sympathetic reader cares because the setup is deliberately favorable yet still produces low rediscovery rates, showing that current prompting does not readily reproduce the bug-finding stories attached to the Mythos materials.

Core claim

Under a target-file scaffold that removes all direct identifiers, systems-specific prompting produces only six target matches across fifty-four counted attempts: five from GPT-5.5 xhigh covering two of six tasks, one from Claude Opus 4.7 covering one task, and zero from Kimi K2. Models routinely submit source-grounded hypotheses that address alternate invariants within the file rather than the specific one corrected by the public Mythos patch evidence.

What carries the argument

The controlled target-file rediscovery experiment that supplies read-only source files, omits all identifying metadata, and applies one manual target-matching rubric to score whether model outputs rediscover the intended Mythos bug.

If this is right

  • Simple prompting with file access is insufficient to rediscover most of the specific Mythos-linked bugs under the tested conditions.
  • Early commitment to plausible alternate candidates inside the assigned file is the main observed failure mode.
  • The results leave open the possibility that Anthropic's original workflow used methods beyond the prompting scaffold examined here.
  • Benchmarking claims that rely on undisclosed internal workflows cannot be directly replicated with standard file-provided prompts.

Where Pith is reading between the lines

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

  • Similar experiments could test whether allowing models to request additional files or run iterative queries raises the rediscovery rate.
  • The pattern of latching onto wrong but source-plausible bugs may appear in other security or correctness tasks where multiple candidate defects exist in one file.
  • If low rediscovery persists across more models, it would suggest that current LLM architectures have structural difficulty isolating a single corrected invariant when many alternatives are present.

Load-bearing premise

The manual target-matching rubric correctly identifies rediscovery of the intended Mythos bug without bias from the specific language or details omitted from the prompts.

What would settle it

A replication in which any model achieves at least nine target matches out of eighteen attempts on the same six tasks while still using only the supplied files and the same rubric would falsify the reported low rediscovery rate.

Figures

Figures reproduced from arXiv: 2605.17416 by Arthur Gervais, Isaac David.

Figure 1
Figure 1. Figure 1: Architecture of the executed target-file rediscovery experiment. The model loop receives [PITH_FULL_IMAGE:figures/full_fig_p005_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Target rediscoveries by model and task. Each cell reports successful target matches out of [PITH_FULL_IMAGE:figures/full_fig_p007_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Resource accounting for the 54 counted attempts: cost by stage, recorded token volume, [PITH_FULL_IMAGE:figures/full_fig_p008_3.png] view at source ↗
read the original abstract

Anthropic's April 2026 Mythos materials combine benchmark claims with concrete bug-finding stories across OpenBSD, FreeBSD, Linux, FFmpeg, and browsers. This paper reports a controlled target-file rediscovery experiment on six public or high-confidence Mythos-linked systems tasks. Each model receives the same target file or files, read-only source tools, three repeats per task, and one manual target-matching rubric; prompts omit CVE identifiers, patch hashes, advisory text, author names, disclosure dates, and answer key root cause language. The experiment contains 54 counted model-task attempts: three models, six tasks, and three repeats, giving 18 attempts per model. GPT-5.5 xhigh achieves 5/18 target rediscoveries, covering 2/6 tasks; counting one wrong-target mpegts.c finding separately gives 3/6 distinct core bugs. Claude Opus 4.7 achieves 1/18 target rediscoveries, covering 1/6 tasks. Kimi K2 records 0/18 target rediscoveries. The dominant failure mode is early commitment to plausible alternate candidates within the assigned file: models often submit source-grounded hypotheses while missing the specific invariant corrected by public Mythos patch evidence. These results do not refute Anthropic's undisclosed workflow, but show that under this favorable target-file scaffold, systems-specific prompting yields only six target matches across 54 counted attempts.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

1 major / 2 minor

Summary. The manuscript reports a controlled target-file rediscovery experiment on six Mythos-linked systems bugs using three models (GPT-5.5 xhigh, Claude Opus 4.7, Kimi K2). Each model receives the same target file(s), read-only source tools, three repeats per task, and a single manual target-matching rubric; prompts deliberately omit CVE identifiers, patch hashes, advisory text, and root-cause language. Across 54 attempts the paper records 5/18 target matches for GPT-5.5 (covering 2/6 tasks, or 3/6 when counting one wrong-target mpegts.c case), 1/18 for Claude, and 0/18 for Kimi, with early commitment to alternate candidates as the dominant failure mode.

Significance. If the counts are robust, the work supplies a repeatable, prompt-controlled benchmark showing that systems-specific prompting under a favorable target-file scaffold yields only six target matches across 54 attempts. The design includes three repeats per task and a defined rubric, which are strengths that support direct model comparison and future replication.

major comments (1)
  1. [description of the manual target-matching rubric] The central counts (5/18, 1/18, 0/18) rest entirely on a single manual target-matching rubric whose decision criteria are not accompanied by inter-rater reliability statistics, a blinded re-scoring protocol, or an explicit decision tree for borderline cases. Because prompts exclude CVE numbers, patch hashes, and root-cause language, the rubric must map model hypotheses to the intended Mythos bug using only the target-file scaffold; without validation of this mapping the reported rediscovery rates remain sensitive to rater judgment rather than model capability alone.
minor comments (2)
  1. [experimental setup] The manuscript would benefit from an explicit statement of the six task-selection criteria and the precise prompt templates to allow readers to evaluate possible selection effects.
  2. [results] Clarify the exact counting rule that treats the mpegts.c case as a separate core bug when computing the 3/6 distinct-bug figure.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for the careful and constructive review of our manuscript. We address the single major comment below and describe the changes we will incorporate in revision.

read point-by-point responses

  1. Referee: [description of the manual target-matching rubric] The central counts (5/18, 1/18, 0/18) rest entirely on a single manual target-matching rubric whose decision criteria are not accompanied by inter-rater reliability statistics, a blinded re-scoring protocol, or an explicit decision tree for borderline cases. Because prompts exclude CVE numbers, patch hashes, and root-cause language, the rubric must map model hypotheses to the intended Mythos bug using only the target-file scaffold; without validation of this mapping the reported rediscovery rates remain sensitive to rater judgment rather than model capability alone.

    Authors: We agree that greater transparency around the rubric strengthens the work. The manuscript (Section 3.3) defines a match as occurring only when a model output identifies both the supplied target file and the precise code location or invariant corrected by the public Mythos patch diff. We acknowledge that formal inter-rater reliability statistics and a blinded re-scoring protocol are absent, as scoring was performed by the lead author with co-author cross-checks on the six tasks. To address the concern directly, the revised manuscript will add an explicit decision tree (file reference required; key modified function/variable named; proposed cause aligned with patch evidence) together with the complete set of 54 model outputs and their classifications. This appendix will allow independent application of the same criteria. While we cannot retroactively perform a new blinded study, the added documentation and raw data substantially reduce dependence on unstated rater judgment. revision: partial

Circularity Check

0 steps flagged

No circularity: direct empirical counts from model runs against fixed rubric

full rationale

The paper reports raw success counts (5/18, 1/18, 0/18) obtained by executing three models on six tasks with three repeats each, then applying one manual target-matching rubric to the 54 outputs. No equations, fitted parameters, first-principles derivations, or predictions are claimed; the central results are direct tallies of whether model hypotheses matched the intended Mythos bug under the stated scaffold. No self-citations appear in the load-bearing steps, and the rubric is presented as an explicit measurement procedure rather than a derived quantity. The derivation chain is therefore self-contained and consists solely of experimental observation.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The work is an empirical benchmark relying on standard assumptions about prompt neutrality and measurement validity rather than new mathematical structure or invented entities.

axioms (1)
  • domain assumption The chosen target files and tasks accurately represent the bugs highlighted in the Mythos materials.
    The validity of measuring rediscovery success depends on these files being the correct reference points for the intended bugs.

pith-pipeline@v0.9.0 · 5779 in / 1175 out tokens · 55049 ms · 2026-05-19T23:11:04.576523+00:00 · methodology

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    Only report a finding you can defend from the source

    Be conservative. Only report a finding you can defend from the source. Return either: A) ‘submit_finding‘ with: - summary: one concise sentence - vulnerable_files: array of repository paths - vulnerable_functions: array of function or method names - root_cause: concrete source-level cause - trigger: attacker-controlled input or precondition - impact: like...

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