| --- |
| license: apache-2.0 |
| task_categories: |
| - text-classification |
| language: |
| - en |
| tags: |
| - security |
| - code-review |
| - benchmark |
| - vulnerability-analysis |
| - evaluation |
| pretty_name: GuardBench |
| size_categories: |
| - n<1K |
| configs: |
| - config_name: default |
| data_files: items.jsonl |
| --- |
| |
| # GuardBench |
|
|
| A benchmark for one question: **can the model follow a guard?** |
|
|
| Every failure worth caring about that we measured on real code in August 2026 was a guard |
| question, in one direction or the other: |
|
|
| - the base and LOREA Pilot announced disabled TLS in code that passes its SSL context correctly, |
| a symlink escape in code with realpath+commonpath confinement ten lines above the call, and a |
| spoofable forwarded header behind a loopback gate. Guard present, guard unread. |
| - Ginko v1 cleared a real timing attack because `startswith("Bearer ")` sat on the tainted path. |
| Guard present, guard irrelevant, cleared anyway. |
|
|
| FBE cannot see either. In a 50-token snippet the guard and the sink are adjacent and the example |
| is too short to miss one, so FBE-safe reports about 3 percent false alarms while the same model |
| gets three of four wrong on a 1,500-line file. GuardBench puts distance between the guard and the |
| sink, because distance is what the failure needs. |
|
|
| ## The six shapes |
|
|
| 120 items: 20 patterns across 19 vulnerability classes, six shapes each. |
|
|
| | shape | guard | correct verdict | what it catches | |
| |---|---|---|---| |
| | `none` | absent | VULNERABLE | baseline: can it find anything | |
| | `covers` | present, applied, sufficient | SAFE | false alarms on defended code | |
| | `covers_alt` | a second correct implementation, defended differently | SAFE | as above, so one wrong answer does not move the rate 12 points | |
| | `wrong_value` | present, applied to a sibling value | VULNERABLE | the tainted value slipped past | |
| | `irrelevant` | present, applied to the right value, does not address the weakness | VULNERABLE | mistaking a format check for a control | |
| | `elsewhere` | defined and used in this file, not on this path | VULNERABLE | helper exists, call site skips it | |
|
|
| `irrelevant` is the shape no existing corpus has and the one Ginko fails. `covers` is the shape |
| the base and Pilot fail. |
|
|
| ## Rules that make it scoreable |
|
|
| **Distance is required.** In every item the guard is a helper defined above, a decorator, a |
| branch several lines up, or a constant declared at module scope. Never the line before the sink. |
|
|
| **Verdicts are parsed, never matched.** Every item requires a final `VERDICT: VULNERABLE <class>` |
| or `VERDICT: SAFE` line. A missing line is a scored failure, not a guess. The FBE grader counted |
| "does not contain any vulnerabilities" as claiming a vulnerability, and no benchmark here will |
| repeat that. |
|
|
| **Three prompt phrasings per item.** Ginko dropped its verdict line on 48 of 65 FBE items purely |
| because the harness said "Analyze this code for security issues" where its training said "Review |
| this code for security problems". A model that only works on one phrasing is not working, and the |
| benchmark should show that rather than reward it. |
|
|
| **Ground truth by construction.** Each item is written as a base plus a delta that defines the |
| label. Nothing here is labelled by a model or by judgement. |
|
|
| **No overlap with anything a model here was trained or selected on.** Checked by 7-gram shingle |
| overlap against all 459 Cyber training seeds and all 169 FBE items: worst overlap 0 percent. One |
| pattern was rewritten when its HMAC helper measured 29 percent against a training seed. |
|
|
| ## Scoring |
|
|
| Report four numbers, and never one alone: |
|
|
| accuracy over all items |
| false alarms on `covers` only - the base/Pilot failure |
| missed on `irrelevant` - the Ginko failure |
| no-verdict rate over all prompt variants - brittleness |
| |
| Verified against degenerate strategies: always-VULNERABLE scores 67 percent raw and **50 percent |
| balanced**; always-SAFE scores 33 percent raw and **50 percent balanced**. Raw accuracy is printed |
| but should never be quoted alone. |
|
|
| ## Running it |
|
|
| python3 guardbench/run.py --tag <name> [--model <path>] [--adapter <path>] [--prompts 1|2|3] |
| |
| Generations are appended to `results/partial_<tag>.jsonl` as they complete, and a re-run skips |
| what is already there. An interrupted run resumes rather than starting over. `--prompts 1` drops |
| the brittleness measurement and cuts the run to a third. |
|
|
| To score a partial or finished run without generating anything: |
|
|
| python3 -c "import json,sys; sys.path.insert(0,'guardbench'); import run; \ |
| rows=[json.loads(l) for l in open('guardbench/results/partial_<tag>.jsonl')]; \ |
| run.report(rows,'<tag>')" |
| |
