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arxiv: 2606.31159 · v1 · pith:JA4B4E3Gnew · submitted 2026-06-30 · 💻 cs.SE · cs.CR· cs.LG

An Empirical Study of Security Calibration in Large Language Models for Code

Mohammed Latif Siddiq , Md. Nafiu Rahman , Joanna C. S. Santos This is my paper

Pith reviewed 2026-07-01 05:01 UTC · model grok-4.3

classification 💻 cs.SE cs.CRcs.LG
keywords security calibrationLLM code generationoverconfidencefunctional correctnessvulnerability remediationmodel confidencesoftware security
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The pith

LLMs for code are overconfident, with calibration stronger for security outcomes than for functional correctness.

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

The paper measures calibration in three LLMs by checking whether their stated confidence matches the actual security and correctness of the code they generate. It runs the models on self-contained security tasks and on multi-language repository contexts at different temperatures. Results show overconfidence is common, and that the models' confidence tracks security vulnerabilities more accurately than it tracks whether the code will execute correctly. The authors also test automated repair guided by calibration scores and several mitigation approaches, finding only modest gains and new problems in realistic settings.

Core claim

Overconfidence is prevalent across the evaluated LLMs. Functional calibration is consistently worse than security calibration, suggesting that models estimate security outcomes more reliably than functional correctness, potentially because functional correctness depends on complex execution behavior. Architectural gating improves calibration on controlled benchmarks but calibration deteriorates in realistic repository-level settings, increasing the risk of high-confidence vulnerable outputs.

What carries the argument

Security calibration versus functional calibration, quantified as the match between model-reported confidence and ground-truth outcomes on the two benchmark suites.

If this is right

  • Calibration-guided repair produces only limited vulnerability fixes and frequently adds functional regressions.
  • Architectural gating reduces false trust on controlled tasks but raises the rate of high-confidence vulnerable code in repository-level contexts.
  • Models appear to estimate security outcomes more reliably than they estimate whether generated code will run correctly.

Where Pith is reading between the lines

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

  • Developers using LLM confidence scores to decide whether to accept generated code may be accepting more functional risk than security risk.
  • Calibration differences could be used to decide when to apply extra static analysis or testing before deployment.
  • Training objectives that directly target functional execution traces might narrow the observed gap between the two calibration types.

Load-bearing premise

The two chosen benchmark suites give representative measures of both security vulnerabilities and functional correctness that extend to other code-generation tasks.

What would settle it

A follow-up evaluation on a different collection of security and functional tasks in which functional calibration matches or exceeds security calibration would undermine the central pattern reported.

Figures

Figures reproduced from arXiv: 2606.31159 by Joanna C. S. Santos, Md. Nafiu Rahman, Mohammed Latif Siddiq.

Figure 1
Figure 1. Figure 1: Overview of our Study Methodology CWE categories and enables precise, automated evaluation of both functional correctness and security. 2) Models & Parameters: Each of the 100 prompts from the SALLM benchmark is provided as input to three models that represent the current landscape of frontier and open-weight architectures [41] GPT-4o-mini [42], an optimized OpenAI model designed for low-latency reasoning … view at source ↗
read the original abstract

Large Language Models (LLMs) are rapidly transforming software development, yet their use in security-critical contexts raises a key question: do models know when their generated code is insecure? This property, known as calibration, measures whether a model's confidence aligns with the true correctness of its outputs. We present the first large-scale empirical study of security calibration in LLM-generated code. We evaluate GPT-4o-mini, Gemini-2.0-Flash, and Qwen3-Coder-Next across multiple temperature settings on two complementary benchmarks: self-contained security tasks and multi-language repository-level contexts. Our results suggest that overconfidence is prevalent across the evaluated LLMs. Functional calibration is consistently worse than security calibration, suggesting that models estimate security outcomes more reliably than functional correctness, potentially because functional correctness depends on complex execution behavior. We also examine whether calibration-guided automated repair can help remediate vulnerabilities in LLM-generated code, finding only limited improvements while frequently introducing functional regressions. Moreover, we study different mitigation strategies for reducing False Trust, where models assign high confidence to vulnerable code. The results show that although architectural gating improves calibration on controlled benchmarks, calibration deteriorates in realistic repository-level settings, increasing the risk of high-confidence vulnerable outputs.

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

2 major / 2 minor

Summary. The paper presents the first large-scale empirical study of security calibration in LLM-generated code. It evaluates GPT-4o-mini, Gemini-2.0-Flash, and Qwen3-Coder-Next across temperature settings on two benchmarks (self-contained security tasks and multi-language repository-level contexts), reporting prevalent overconfidence, consistently better security calibration than functional calibration, limited gains from calibration-guided repair (with frequent functional regressions), and deterioration of mitigation strategies like architectural gating when moving from controlled to realistic repository settings.

Significance. If the results hold, the work provides actionable evidence that LLMs tend to be overconfident about insecure code and that security self-assessment is more reliable than functional correctness assessment. This has direct implications for safe deployment of code-generating LLMs in security-critical contexts and motivates further research on calibration-aware generation and repair techniques.

major comments (2)
  1. [Evaluation Setup / Benchmarks] The central claims (prevalent overconfidence; security calibration reliably superior to functional) rest on the accuracy of ground-truth labels for vulnerability presence and functional correctness in the two benchmarks. The abstract and evaluation description provide no details on sample sizes, labeling procedures (automated vs. manual), inter-rater reliability, or error rates; without these, the reported calibration gap could be an artifact of noisy or non-representative labels rather than a model property.
  2. [Repair Experiments] The claim that calibration-guided automated repair yields "only limited improvements" while "frequently introducing functional regressions" requires quantitative support on the magnitude of regressions and the baseline repair success rate. The abstract does not report effect sizes, statistical significance, or controls for temperature and model, making it impossible to judge whether the limited benefit is robust.
minor comments (2)
  1. [Abstract] The abstract states results "suggest" overconfidence and a calibration gap; the manuscript should clarify whether these are statistically tested differences or descriptive observations.
  2. [Models] Model names (Qwen3-Coder-Next, Gemini-2.0-Flash) should be accompanied by exact version identifiers and access dates to ensure reproducibility.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback and the positive assessment of the work's significance. We address each major comment below, indicating where revisions will be made to strengthen the manuscript.

read point-by-point responses

  1. Referee: [Evaluation Setup / Benchmarks] The central claims (prevalent overconfidence; security calibration reliably superior to functional) rest on the accuracy of ground-truth labels for vulnerability presence and functional correctness in the two benchmarks. The abstract and evaluation description provide no details on sample sizes, labeling procedures (automated vs. manual), inter-rater reliability, or error rates; without these, the reported calibration gap could be an artifact of noisy or non-representative labels rather than a model property.

    Authors: We agree that the abstract and high-level evaluation description omit explicit details on labeling methodology, which is a valid concern for assessing label quality. The full manuscript describes the two benchmarks and their sources but does not dedicate sufficient space to sample sizes, automated vs. manual procedures, inter-rater metrics, or measured error rates. In the revised version we will add a dedicated subsection in the evaluation setup that reports these elements (including exact sample counts per benchmark, the static analysis tools employed, the size and protocol of any manual verification, and any available reliability statistics). This addition will directly address the possibility of label noise affecting the observed calibration differences. revision: yes

  2. Referee: [Repair Experiments] The claim that calibration-guided automated repair yields "only limited improvements" while "frequently introducing functional regressions" requires quantitative support on the magnitude of regressions and the baseline repair success rate. The abstract does not report effect sizes, statistical significance, or controls for temperature and model, making it impossible to judge whether the limited benefit is robust.

    Authors: We accept that the abstract summarizes the repair outcomes at a high level without the requested quantitative details. The full results section already breaks down repair success and regression rates by model and temperature, but does not include effect sizes or formal significance tests. In the revision we will augment the repair experiment subsection with effect-size calculations, statistical significance results, and explicit confirmation that all comparisons control for temperature and model. These additions will provide the quantitative support needed to evaluate the robustness of the "limited improvements" and "functional regressions" findings. revision: yes

Circularity Check

0 steps flagged

Empirical measurement study with no derivations or self-referential reductions

full rationale

The paper is a large-scale empirical evaluation of calibration in three LLMs across two benchmarks, reporting observed overconfidence rates, security-vs-functional differences, and mitigation outcomes. No equations, fitted parameters, uniqueness theorems, or ansatzes are presented as deriving new results; all claims rest on direct experimental measurements. The central findings (prevalent overconfidence; security calibration better than functional) are statistical summaries of the collected data rather than reductions to prior self-citations or input definitions. Benchmark labeling assumptions are external validity concerns, not circularity.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claims rest on the assumption that the chosen benchmarks validly capture security and functional properties; no free parameters or invented entities are described in the abstract.

axioms (1)
  • domain assumption The selected benchmarks accurately reflect real-world security vulnerabilities and functional correctness.
    The study interprets calibration differences between self-contained tasks and repository-level contexts as meaningful only if the benchmarks are representative.

pith-pipeline@v0.9.1-grok · 5755 in / 1108 out tokens · 35500 ms · 2026-07-01T05:01:43.214142+00:00 · methodology

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