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.

翻译：先前的研究表明，在威胁导向场景中，功能正确但存在漏洞的输出会系统性出现，此时对抗性或隐式通道被用来诱发代码代理与自动补丁流程中的安全故障。本文提出一种互补但不同的框架：虚假安全信心（False Security Confidence, FSC），该框架从测量优先的视角出发，在常规、非攻击框架下的生成任务中研究同一表面现象。我们的关注点不在于攻击能否产生此类输出，而在于明确缺少攻击压力时它们出现的频率与形式，以及常规功能评估是否能可靠检测这些输出。我们将FSC率形式化为功能正确输出集合中安全故障的普遍性，将其与SAFE等联合功能安全指标及CWEval等结果驱动评估框架加以区分，定义了用于研究FSC如何跨通用编程、部署上下文任务及安全显式编程三种生态系统任务视图，并识别出FSC-hard作为一个实际重要的细化层——其中静态分析器遗漏了仍可被动态触发的漏洞。本技术报告有意识地限定为框架性陈述而非完整实证论文：其目的是为后续大规模评估建立术语、测量边界及研究设计方案。