Deep learning for vulnerability detection has shown promising results on early benchmarks, but recent evaluations reveal catastrophic degradation: models achieving F1 > 0.68 on legacy datasets collapse to 0.031 under strict deduplication. We identify the root cause as the semantic ambiguity problem: identical code can be secure or vulnerable depending on project-specific behavioral contracts, rendering global classification fundamentally inadequate. We propose Phoenix, a training-free multi-agent framework that resolves this ambiguity through Behavioral Contract Synthesis. Phoenix decomposes detection into three stages: a Semantic Slicer extracting minimal vulnerability-relevant context, a Requirement Reverse Engineer synthesizing Gherkin behavioral specifications encoding the security contract, and a Contract Judge evaluating code against these specifications via strict compliance checking. On PrimeVul Paired, Phoenix achieves F1 = 0.825 and Pair-Correct = 64.4%, surpassing RASM-Vul (F1 = 0.668) and VulTrial (F1 = 0.563) while using open-source models up to 48x smaller (7-14B vs. 671B). Ablation across 25 configurations demonstrates Gherkin specifications as the decisive driver (+0.09 to +0.35 F1). Error analysis reveals 18% of "False Positives" identify genuine security concerns in patched code, demonstrating that security is a relative property defined against behavioral contracts, not an absolute property of code syntax.