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Abstract

LLM-based vulnerability detectors have shown promising results in identifying memory-safety bugs and wellestablished vulnerability classes, where violations can often be expressed in terms of established security properties such as unsafe data propagation, bounds violations, or invalid memory accesses. Logic vulnerabilities, however, pose a fundamentally different challenge, as their identification requires inferring application-specific security invariants and often relies on implicit assumptions about intended behavior. Even frontier agentic models struggle in this setting, despite their ability to inspect and traverse large repositories, because the security invariants relevant to these vulnerabilities are often implicit and buried among large amounts of unrelated code. Motivated by this gap, we present ANTAEUS, a framework for detecting logic vulnerabilities that grounds LLM reasoning in repository-level code context. ANTAEUS follows a repositoryscale pipeline that combines function prioritization, contextgrounded reasoning, comparative validation, and structured reporting. First, it ranks functions using lightweight repositorywide security signals, directing costly LLM analysis toward the most relevant code regions and reducing model calls, cost, and triage effort. For each prioritized function, ANTAEUS grounds the model in explicit repository evidence, combining local code context with a repository-level view of the applications functionality, security-relevant resources, and trust boundaries. This grounded input enables the model to reason about how the function is executed within the broader application rather than as an isolated snippet. ANTAEUS then identifies securitysensitive sinks, derives the safety conditions required for safe execution, and checks whether those conditions are locally satisfied. Candidate findings are subjected to comparative validation, which prunes concerns that reflect project-wide norms rather than distinctive violations. Finally, ANTAEUS reports the sinks, the violated safety conditions, and the supporting evidence, making findings specific, actionable, and traceable. We evaluate ANTAEUS on 28 real-world repositories with confirmed logic vulnerabilities and compare it against both function-level LLM analysis and frontier agentic models, including Opus 4.8 Agentic and Codex 5.4. ANTAEUS detects and explains 15 vulnerabilities, substantially outperforming stateof-the-art baselines while maintaining a comparable token usage and cost budget.