False Security Confidence in Benign LLM Code Generation

Abstract: Prior work has demonstrated that functionally correct yet vulnerable outputs arise systematically in threat-oriented settings, where adversarial or implicit channels are used to induce security failures in code agents and automated patching workflows. This note introduces a complementary but distinct framing: False Security Confidence (FSC), which studies the same surface phenomenon from a measurement-first perspective in ordinary, non-attack-framed generation tasks. Our interest is not in whether attacks can produce such outputs, but in how frequently and in what forms they appear absent explicit attack pressure, and whether conventional functional evaluation reliably detects them. We formalize FSC rate as the prevalence of security failure within the set of functionally correct outputs, distinguish it from prior joint functional-security metrics such as SAFE and outcome-driven evaluation frameworks such as CWEval, define a three-ecosystem task view for studying how FSC manifests across general-purpose programming, deployment-context tasks, and security-explicit programming, and identify FSC-hard as a practically important refinement layer in which static analyzers miss vulnerabilities that remain dynamically triggerable. This technical report is intentionally scoped as a framework statement rather than a full empirical paper: its purpose is to establish terminology, measurement boundaries, and study design commitments for subsequent large-scale evaluation.

• The paper presents FSC, a metric that quantifies when functionally correct LLM code secretly harbors exploitable security vulnerabilities.
• The methodology employs layered static, dynamic, and manual validation to isolate FSC-hard cases that elude conventional security checks.
• The study recommends ecosystem-specific evaluation and joint metric reporting to better align functional correctness with true security assurance.

False Security Confidence in Benign LLM Code Generation

Motivation and Conceptual Foundations

This report introduces the concept of False Security Confidence (FSC) in the context of code generation by LLMs, articulating a measurement-first framework for quantifying and analyzing security failures that are masked by conventional definitions of correctness. The central premise is that functional correctness and security correctness are not reliably aligned in practice: models routinely emit code that satisfies test oracles but remains exploitable due to security defects. Prior work has documented the functionally-correct-yet-vulnerable phenomenon, particularly in adversarial or threat-driven scenarios (Peng et al., 15 Oct 2025), but FSC formalizes a measurement approach tailored to benign, non-attack-framed tasks.

FSC focuses on instances where functional criteria would tempt a user or evaluator to trust the output, even though latent vulnerabilities persist. The framework characterizes the prevalence and forms of these mismatches, distinct from raw vulnerability rates and joint evaluation metrics such as SAFE (Dai et al., 18 Mar 2025) or CWEval (Peng et al., 14 Jan 2025).

Formalization of FSC Metrics

A precise formalization is provided for evaluating when a generation $s$ exhibits FSC: it must pass all functional correctness checks while failing a defined set of security validation procedures. The key definitional contribution is the FSC rate metric, computed as the conditional probability that, given a sample is functionally correct, it is nonetheless security-failing. This denominator choice eliminates confounding with overall model performance and isolates the frequency of over-trusted security failures among candidate outputs.

The articulation of FSC rate as a conditional metric shifts the lens from aggregate vulnerability rates to judiciously measuring risk in “successful” outputs. The paper strongly recommends joint reporting of FSC rate with functional correctness and insecure-code rates, and emphasizes the need to annotate analyzer coverage and manually adjudicate ambiguous or high-impact cases in empirical studies.

Three-Ecosystem Task Partitioning

Recognizing that causes of FSC may diverge across contexts, the report introduces a three-ecosystem taxonomy for measurement:

• General-Purpose Programming: Standard algorithmic and data-processing tasks without explicit security framing, where security pitfalls may be masked by narrowly defined outputs.
• Deployment-Context Tasks: Scenarios involving secrets management, request routing, environment assumptions, or configuration handling, where functional code can fail security requirements emergent only under realistic deployment.
• Security-Explicit Programming: Tasks deliberately targeting cryptography, validation, or access control, where the model can appear to fulfill a security-aware prompt while the implementation remains exploitable.

This partitioning is motivated both operationally and scientifically; it is hypothesized that each class surfaces distinct patterns of FSC incidence and causal factors. The approach extends beyond existing benchmarks such as SafeGenBench (Li et al., 6 Jun 2025), which typically foreground prompt categories rather than deployment contextuality.

The FSC-hard Subclass

An additional contribution is the definition of FSC-hard: functionally correct yet insecure samples in which vulnerabilities evade detection by standard static analyzers and are only revealed via dynamic or semantic validation. FSC-hard marks the region where security risks are maximized due to coincident functional success, silent static analysis, and persistent exploitability. These cases are precisely where industry evaluation pipelines are most likely to undercount risk, and where strongest tool support deficits exist. Comparing FSC-hard incidence across models and tasks offers a critical axis for future evaluation.

Relation to Prior Work and Non-Goals

The framework is deliberately scoped. It does not encompass adversarially induced vulnerabilities, repair-stage interventions, or internal model reasoning analyses (which are handled in companion works such as Pseudo-Repair and SCS-Code (Wendlinger et al., 11 Mar 2026)). Nor does it seek to redefine leaderboard evaluation—rather, it complements method-comparison metrics like SAFE and outcome frameworks like CWEval by conditioning on the trust zone where FSC meaningfully quantifies overestimation of model quality.

Recent studies have observed that LLMs may acquire internal representations of security concepts without consistent behavioral alignment (Wendlinger et al., 11 Mar 2026); FSC is orthogonal, as it systematically quantifies the extent of exploitable artifacts missed by functional scoring alone.

Recommendations for Empirical Validation

The report outlines rigorous guidance for empirical validation. Functional correctness must be established via task-specific oracles, not heuristics. Security correctness should utilize layered validation pipelines: multiple static analyzers, dynamic exploit tests, and manual review as necessary. For FSC-hard, dynamic or concrete exploit demonstrations are required; tool warnings or pattern matching are insufficient.

The principle is to calibrate validation cost to research aims: minimal credible stacks for general FSC measurement, and maximal semantic adversarial stacks when substantiating FSC-hard claims.

Implications and Directions for Future Research

The introduction of FSC reorients evaluation of code generative models by foregrounding the conditional risk of insecure code passing as trustworthy. This has immediate implications for both benchmark curation and model deployment: it highlights that traditional leaderboard success on functional test sets may systematically overstate model reliability for secure engineering practice.

FSC rate and FSC-hard prevalence constitute new axes for risk quantification, refining the interpretability of empirical studies. In deployment contexts, practitioners are advised to incorporate FSC analysis when evaluating LLM-assisted development pipelines. The ecosystem-level task specification strategized here suggests promising directions for targeted mitigation efforts, especially in dynamically sensitive or deployment-dependent code paths.

Future research is necessitated on several fronts: large-scale FSC quantification across language pairs, validation of FSC-hard incidence with simulative exploit agents, and mechanistic studies connecting observed FSC cases to internal model representations and generation priorities.

Conclusion

False Security Confidence formalizes a conditional security-failure concept that systematizes evaluation of LLM-generated code beyond standard functional benchmarks. By defining FSC and FSC-hard, and establishing an ecosystem-oriented evaluation framework, this report provides terminology and methodology for studying security failures obscured by functional success. These constructs offer a pathway to more robust metrics and validation standards for secure code generation, with significant implications for the design, deployment, and scientific evaluation of LLM-based code agents.