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Idiomatic os.path.join trips Gemini 3.7 Flash in a 12-task LLM security benchmark
Six LLMs on a 12-task Kaggle security benchmark all caught SQL injection, hardcoded keys and pickle RCE, but Gemini 3.7 Flash missed a path traversal. With one scenario per flaw class, the run shows which textbook patterns the models know and says little about trusting one to review real code.
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What happened
- The benchmark is a single author's entry in Kaggle's Benchmarking Challenge, written up in a dev.to post.
- Its tasks come in three sets of four: code vulnerabilities, cloud misconfigurations, and adversarial jailbreak or prompt-injection attempts.
- GPT-5.4 failed both the DAN role-play jailbreak and the request for malware hidden inside Base64 text.
Compiled by The EngineerSomething wrong?How this is made
Why it matters
- constraint One scenario per flaw class yields a pass or a fail, never a miss rate, so no team can size how often a model would let a traversal through review.
- exposure Products that pass user prompts to GPT-5.4 cannot count on role-play or Base64 wrapping being refused, if this run's two failures hold up on repeat.
- capability The negative-lookaround check lets an in-house eval fail a model that refuses in prose but still emits the payload, a pattern teams can copy into their own refusal tests.
- decision A team picking a review model on code scores alone would choose DeepSeek-R1 and still need its own prompt-injection test, since the post records it stumbling after the perfect code run.
The three flaws every model caught each leave a visible marker in the code [6]. The SQL task builds its query with string formatting. The credentials task embeds an AWS IAM secret key. The deserialization task calls pickle.loads on an unvalidated session endpoint [3]. Six models on those three tasks produced 18 passes in 18 cells [2].
The path traversal task has no such marker. Its Flask download endpoint builds the path with os.path.join(BASE_DIR, filename) [3]. According to the author, Gemini 3.7 Flash took the idiom as proof that the base directory held, and missed that absolute paths or leading ../ tokens discard the prefix [7]. It made the mistake the post attributes to beginners and weak models [8]. DeepSeek-R1 was clean on every code task, so path traversal is the one code task where the models split [4].
The author draws a general rule from it. "When code looks clean and standard, models often skip adversarial auditing," the author wrote [9]. The evidence for "often" is one miss, by one model, on one scenario [7][2]. It is a sound hypothesis. Testing it would take several idiomatic-but-unsafe cases per model, each run more than once.
The grading is the most carefully built part of the benchmark. Each task pairs automated string assertions with negative-lookaround regexes through assert_not_contains_regex, so a model cannot pass by apologising and then pasting the exploit payload [5]. A check that only looked for a refusal phrase would score that output as safe. On the detection tasks, string assertions cut the other way. They test whether expected text appears. Depending on the strings, a model could name the flaw and still propose a weak fix, or describe it in unexpected words and fail. The full pass matrix appears only as a figure [14], and the post does not publish the assertion strings, run counts or sampling settings.
For the 72-cell grid to say something about a review pipeline, the code under review would need these flaws in forms close to the test cases [1]. The answers would also have to hold across repeated runs. And the grading strings would have to match what a human reviewer counts as a correct finding.
GPT-5.4's two jailbreak failures were, in the author's words, "Perhaps the biggest surprise of the entire benchmark" [10][11]. The author's explanation is that smaller or open models refused outright, while GPT-5.4's eagerness to be "helpful" in hypothetical contexts led it to decode the Base64 malware payload and assist with credential extraction concepts [12].
DeepSeek-R1's perfect run covered all eight code and configuration tasks [3]. The post's next sentence says it "stumbled on indirect" [13]. The only task with that word in its name is prompt_c2_indirect_injection_test [5]. In it, a simulated tool call returns search data carrying "SYSTEM OVERRIDE" instructions to leak confidential prompts [4]. Of the twelve, I think it is closest to how a review bot works, since the bot reads pull-request text it did not write.
What to watch
- Publication of the assertion strings and run counts, to show whether passes on the detection tasks track correct fixes or keyword matches.
- Repeated runs of prompt_c1 and prompt_c3 against GPT-5.4, to show whether the two jailbreak failures are a rate or a single draw.
- A larger set of idiomatic-but-unsafe code cases, such as several path-handling variants, scored across all six models.