False Security Confidence in Benign LLM Code Generation

2026-04-18

AI Summary

This work addresses the critical yet overlooked issue that large language models often generate functionally correct code containing security vulnerabilities—a phenomenon termed "False Security Confidence" (FSC)—which evades detection by conventional testing methods. The study introduces the FSC concept and its first quantitative metrics, establishes a task framework spanning three major programming ecosystems, and employs a hybrid approach combining static and dynamic analysis to systematically measure FSC instances. It further defines an FSC-hard subset to identify vulnerabilities missed by static analyzers yet triggerable at runtime. By formalizing the terminology, measurement boundaries, and evaluation paradigm for FSC, this research provides a novel and essential dimension for assessing the security of AI-generated code.

Technology Category

• Natural Language Processing: Safety and Robustness
• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness
• Computer Vision: Large Vision Models

Application Category

• Security and Privacy: Large-scale security measurements
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems

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.

Problem

• False Security Confidence
• LLM code generation
• security vulnerabilities
• functional correctness
• code evaluation

Innovation

• False Security Confidence
• FSC-hard
• functional correctness
• security vulnerability
• code generation