Detection rate alone ranks the worst LLM code reviewers first

Sunny Chokshi — Wed, 28 Oct 2026 19:31:01 +0000

When I tested 16 open LLM code reviewers, the first table in my paper showed which of them separate secure code from vulnerable code at all. It is worth reading on its own.

The setup

I used 180 Python snippets across nine cryptographic misuse classes, ten pairs per class. Each pair has a vulnerable snippet and a secure twin that does the same task correctly. I ran 16 open models, from 0.5B to 120B parameters, with three repeats per snippet, which gives 8,640 scored calls. For each model I report the true positive rate (TPR, vulnerable snippets flagged), the false positive rate (FPR, secure twins flagged), and balanced accuracy, the average of TPR and one minus FPR. A model that flags everything scores 0.5.

Why detection rate misleads

Judged on TPR alone, codellama 7B, devstral 24B, and qwen2.5-coder 0.5B would rank near the top, each at or near 100%. Their FPRs tell a different story. codellama 7B and qwen2.5-coder 0.5B flagged 539 and 540 of 540 scored calls. devstral 24B flagged 82.6% of the secure twins. Three more models (codellama 34B, qwen2.5-coder 1.5B and 3B) also flagged more than 70% of secure code. The 0.5B model also left out the requested verdict line in all but 37 of its 540 replies.

With the secure twins in the table, the order changes. Of the 16 models, gpt-oss 20B had the highest balanced accuracy, 0.952, with a TPR of 93.7% and an FPR of 3.3%. Ranked by TPR alone, these models would sit at or near the top.

Does model size help?

Size and score go together to some degree, but size does not settle the ranking. Over all 16 models, parameter count and balanced accuracy are related (Spearman 0.76). Across families it did not order the results: codellama 34B (0.609) and devstral 24B (0.587) were among the weakest of the larger models. Even within gpt-oss, the 120B model (0.931) scored below the 20B (0.952).

What I take from this test: a detection rate on its own rewarded models that said "vulnerable" almost every time. I would report the false positive rate next to it and use secure twins that match the vulnerable code.

Limits

The snippets are short Python functions, reviewed one at a time, so the result speaks to a single-snippet review pass. The models are open models run with temperature 0. I tested only open-weight models and only Python, so I make no claim about closed models or other languages. TPR counts a vulnerable verdict given for any reason, and I did not check whether each detection named the right misuse. This is a preprint, it is not peer reviewed, and it carries the notice that it has been submitted to the IEEE for possible publication.

The same paper goes on to test what happens when the same misuse is written indirectly. I covered one result from that part in my earlier post on a hex key size.

Read and cite

Preprint on Zenodo (doi:10.5281/zenodo.23351769) · Paper page and PDF

Related work in the same line, an earlier benchmark: CryptoBench (doi:10.5281/zenodo.23067051).

Cite as: Chokshi, S. (2026). Does Indirection Hurt? Paired Evidence on LLM Review of Indirectly Written Cryptographic API Misuse [Preprint]. Zenodo. https://doi.org/10.5281/zenodo.23351769

Dataset: Chokshi, S. (2026). CryptoBench: A benchmark of cryptographic API misuse for evaluating LLM code reviewers [Dataset]. Zenodo. https://doi.org/10.5281/zenodo.23067051

Sunny Chokshi, PhD student, Information Technology, University of the Cumberlands. Research: adversarial robustness of LLMs in cybersecurity. ORCID · GitHub

Originally published at https://sunny-chokshi.github.io/blog/reviewer-that-flags-everything.html

A hex literal made LLM code reviewers raise false alarms

Sunny Chokshi — Wed, 28 Oct 2026 19:30:33 +0000

I wanted to know whether an LLM code reviewer follows what the code does or what the code looks like. Cryptographic API misuse is a clean place to ask, because the same insecure choice can be written in many ways.

The setup

I used 90 pairs of Python snippets across nine misuse classes. Each pair holds a vulnerable snippet and a secure twin. For 80 pairs I wrote a plain form, with the misuse at the call site under its familiar name, and an indirect form that reaches the same choice through an alias, constant, default, or configuration value. I ran 16 open models.

The secure twins already showed a problem. Two models flag nearly every snippet, and four more flag over 70% of secure code. A reviewer that flags nearly everything tells me little about the code.

What indirection did to the scores

Indirection lowered the balanced accuracy of the seven strongest models by 4.5 points over the 80 pairs (pair-cluster 95% CI 1.3 to 7.6). On the 47 pairs that survive a post hoc stimulus audit, the drop was 5.3 points (CI 2.1 to 8.5).

Where the drop comes from needs care. After the audit it is mainly extra flags on correct code. False positives rose 9.1 points (CI 4.2 to 14.6), mostly in three models. Detection of real misuse fell 3.6 points over all 80 pairs (CI 1.0 to 6.5), but only 1.5 points on the audited pairs, and that interval (CI -1.6 to 4.8) includes zero.

Where the false alarms sit

Exploratory splits point at where the false alarms come from. Most of the net false-alarm rise sits in pairs where the safe value has left its usual spelling or place at the call site. A 3072-bit key size written as 0xC00 is the example I keep coming back to. My reading is that the models react to an unfamiliar spelling instead of the value. The paper does not test that directly.

Limits

The snippets are Python and I wrote them. The models are open models. The audit was post hoc and the splits are exploratory. This is a preprint, it is not peer reviewed, and it carries the notice that it has been submitted to the IEEE for possible publication.

Open question for the next study: do the extra flags go away if the reviewer is told to normalize values before judging? I have not tested that.

Read and cite

Preprint on Zenodo (doi:10.5281/zenodo.23351769) · Paper page and PDF

Related work in the same line, an earlier benchmark: CryptoBench (doi:10.5281/zenodo.23067051).

Cite as: Chokshi, S. (2026). Does Indirection Hurt? Paired Evidence on LLM Review of Indirectly Written Cryptographic API Misuse [Preprint]. Zenodo. https://doi.org/10.5281/zenodo.23351769

Dataset: Chokshi, S. (2026). CryptoBench: A benchmark of cryptographic API misuse for evaluating LLM code reviewers [Dataset]. Zenodo. https://doi.org/10.5281/zenodo.23067051

Sunny Chokshi, PhD student, Information Technology, University of the Cumberlands. Research: adversarial robustness of LLMs in cybersecurity. ORCID · GitHub

Originally published at https://sunny-chokshi.github.io/blog/hex-key-size-false-alarms.html

DEV Community: Sunny Chokshi

Detection rate alone ranks the worst LLM code reviewers first

The setup

Why detection rate misleads

Does model size help?

Limits

Read and cite

A hex literal made LLM code reviewers raise false alarms

The setup

What indirection did to the scores

Where the false alarms sit

Limits

Read and cite