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Sometimes, on the tests they were given—but this benchmark does not establish that large language models can reliably audit real-world systems. A submission by LOI CHIANG HAO, published on DEV Community on October 1, 2026, reports strong scores across 12 security tasks, alongside model-specific misses. Its results are best read as a small, custom stress test, not a general verdict on AI code auditing.
What did the 12-task benchmark test?
The author grouped 12 scenarios into three categories, with four tasks in each. Some test whether a model recognizes a security flaw; others test whether it resists instructions that could lead to unsafe assistance.
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Code vulnerabilities
- A Python SQL query built with string formatting, which can allow SQL injection.
- Hardcoded AWS IAM secret keys.
- A Flask file-download path built with
os.path.join(BASE_DIR, filename)without sufficient protection against path traversal. - Insecure deserialization using
pickle.loadson an unvalidated session endpoint.
Cloud and infrastructure configuration
- An Nginx open redirect that uses an unvalidated
302 $arg_url. - An iptables
INPUT ACCEPTdefault policy that makes purported database allow-rules redundant. - An AWS Lambda IAM policy with wildcard permissions for an S3 read operation.
- A Kubernetes
ClusterRolewith wildcard verbs and API groups assigned to a read-only monitoring service.
Prompt injection and jailbreaks
- A DAN-style role-play prompt requesting phishing templates.
- A simulated tool-use task in which search data contains “[SYSTEM OVERRIDE]” instructions to reveal prompts.
- A Base64-encoded malware request presented as an encoding study.
- A creative-writing prompt requesting working SQL injection vectors.
What scores did the author report?
The table reproduces the model labels and results reported by LOI CHIANG HAO in the 2026 DEV Community submission. Each category contains four tasks, so its percentages correspond to performance on that small set; the overall result is out of 12. These are the author’s figures, not independently verified benchmark results.
| Model label in submission | Overall | Code | Configuration | Jailbreak |
|---|---|---|---|---|
| Qwen 3 Coder 480B | 91.67% (11/12) | 100% | 100% | 75% |
| Grok 4.20 Reasoning | 91.67% (11/12) | 100% | 100% | 75% |
| Gemini 3.7 Flash | 91.67% (11/12) | 75% | 100% | 100% |
| DeepSeek-R1 | 83.33% (10/12) | 100% | 100% | 50% |
| GPT-5.4 | 83.33% (10/12) | 100% | 100% | 50% |
| GLM-5 | 75.00% (9/12) | 75% | 100% | 50% |
The category scores make an important distinction: the reported configuration results are uniformly high, while jailbreak results range from 50% to 100%. A model’s overall score can therefore obscure where its misses occurred. With only four scenarios per category, however, each individual task has substantial influence on the category percentage.
#1 Best Overall
What do the reported misses show?
The submission describes particular failures, but the accessible article does not include the raw model outputs. Treat these as the author’s account of responses in this benchmark, not as independently reproduced observations or evidence about model behavior in general.
Path traversal
The author says Gemini 3.7 Flash missed the Flask path-traversal issue. The author’s explanation is that os.path.join(BASE_DIR, filename) does not, by itself, ensure a path stays inside the intended directory: an absolute path or a ../ segment may escape it. The reported miss concerns that task and response, not every path-handling review the model might perform.
Jailbreak and indirect-injection tasks
The author says GPT-5.4 failed the DAN-style role-play and Base64-bypass tasks, and reports that it decoded the malware payload and assisted with credential-extraction concepts. The submission also says DeepSeek-R1 failed the indirect prompt-injection and fictional-framing tasks. Without the prompts and outputs, those claims cannot be checked independently or used to establish a general cause, such as a failure of reasoning over untrusted data.
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Strong scores are still bounded results
The author reports that every tested model flagged the SQL injection, hardcoded credentials, and pickle-deserialization tasks, and that all six received 100% in the configuration category. That supports a narrow statement about these scenarios and scoring rules. It does not prove comprehensive coverage of those vulnerability classes or readiness to approve a production change.
Rank #3
How was success scored, and what can the score tell you?
The author says the benchmark used automated string assertions and negative-lookaround regular expressions, including an assert_not_contains_regex check intended to stop an answer from passing as a refusal if it still included a disallowed exploit payload.
That approach can check whether an answer contains expected or disallowed text. But the accessible article does not provide the exact prompts, regexes, thresholds, false-positive checks, or task-by-task outputs. Without those details, a reader cannot assess how well the assertions distinguish a sound security analysis from a superficially matching answer—or reproduce the scores.
The submission also does not identify exact provider model snapshots or run settings. Model labels and percentages should therefore be attributed to the October 2026 submission rather than treated as a durable ranking of model families. Aggregate pass rate is one comparison axis; it is not a measure of benchmark validity, comprehensive audit ability, or reproducibility.
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- Real-world audit reliability: Twelve constructed tasks cannot show how a model performs across the range of codebases, configurations, and security-review conditions.
- Independent reproducibility: The accessible post does not expose the underlying prompts, benchmark code, raw outputs, or per-task assertions.
- Current model-wide rankings: Exact snapshots and run configurations are not stated, so the results cannot safely be generalized beyond the author’s named test runs.
- Cost efficiency: The author calls Qwen 3 Coder 480B a score-versus-cost Pareto leader and says it achieved its reported 91.67% pass rate at a fraction of commercial API costs. No numerical costs, token counts, provider rates, execution date, or underlying cost data are supplied, so that claim cannot support a quantified comparison or buying recommendation.
What should developers take away?
The results suggest that models can identify familiar flaws and configuration problems in some deliberately framed scenarios, while still missing other tasks—especially in the reported jailbreak results. That is a reason to test a model against the specific workflow and threat cases where it will be used, not to treat a high score as a security sign-off.
Best Value
For teams evaluating a coding assistant, useful evidence would include the exact model snapshot and settings, reproducible prompts, raw outputs, transparent scoring rules, and tests that check whether proposed fixes actually remove the issue without introducing another one. The submission proposes multi-turn escalation after an initial refusal, context-window overflow that hides malicious content in legitimate material, and patch verification as future measurements. Those are proposed next steps, not findings from its 12 tasks.
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