Self-Discovering Security Oracles: Meta-learning Vulnerability Detection Strategies through Adversarial Self-Play

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Current LLM-based vulnerability detection systems rely on static prompting strategies and fixed analysis pipelines, which fail to adapt to an evolving threat landscape. These approaches struggle to discover unknown vulnerability patterns beyond their training distributions, limiting their effectiveness against emerging security threats. This paper investigates adversarial meta-learning as a way for foundation models to autonomously discover vulnerability detection strategies through reinforcement learning and competitive self-play. We present MetaMorphSES (Meta-learning through Morphological Security Evolution and Synthesis), a framework that enables foundation models to jointly evolve vulnerability generation capabilities (red team) and detection strategies (blue team) via adversarial co-evolution, without predetermined prompts or fixed architectures. The system learns to generate increasingly sophisticated synthetic vulnerabilities while simultaneously developing detection strategies that generalize to real-world security flaws. Through a dual-loop optimization process, the inner loop generates and detects vulnerabilities, while the outer meta-learning loop optimizes detection strategies via reinforcement learning. Experiments on six real-world vulnerability corpora show that the meta-learned security strategies substantially improve detection of unseen vulnerabilities (up to 0.90 F1) over nine strong baselines spanning rule-based static analysis, zero-shot and fine-tuned LLMs, and recent neural vulnerability detectors. The system converges to a stable equilibrium between attack and defense capabilities. While training remains resource-intensive, our results establish adversarial meta-learning as a novel and effective paradigm for discovering security vulnerabilities with foundation models through self-directed adaptation, without explicitly programmed detection rules.

Last synced 2026-10-15 02:32 UTC