Path-sensitive vulnerabilities, such as use-after-free, integer overflows, and command injection, pose significant challenges for traditional static analysis tools, which often face trade-offs between precision, scalability, and interpretability. To address these challenges, we present SEVDF (Semantic-Enhanced Vulnerability Detection Framework), a novel methodology that integrates may-analysis taint propagation with large language models (LLMs) to detect path-related vulnerabilities in large C/C++ codebases. SEVDF begins by constructing a program dependency graph and performing a sound but incomplete taint analysis to extract all potential vulnerable paths. After segmentation, deduplication, feasibility check, and semantic summarization by LLMs, the vulnerable paths are reformed and confirmed with LLMs for their inter-procedural feasibility and semantic consistency. We evaluate SEVDF on the Juliet Test Suite (thirteen CWE categories) and a curated real-world dataset of 71 vulnerabilities across 9 projects. SEVDF consistently outperforms the default CodeQL rules, CodeQL rules with all unnecessary constraints removed, and three open-source detectors, which are Infer, Cppcheck and CodeChecker. SEVDF is able to achieve 100% precision on several CWEs while maintaining or improving recall on Juliet benchmark. Moreover, our segment-based design reduces the analysis workload for LLMs by 90.6% compared to direct-path prompting through Logic Unit deduplication, making SEVDF cost-effective for large-scale deployment. Finally, SEVDF uncovered and reported 29 0-day vulnerabilities (12 confirmed to date), including 3 CVEs in VirtualBox, demonstrating practical value.

The PHP interpreter, powering over 70% of web-sites on the internet, plays a crucial role in web development. Existing approaches to finding bugs in PHP primarily focus on detecting explicit security issues through crashes or sanitizer-based oracles, but fail to identify logic bugs that can silently lead to incorrect results. We observe that the introduction of Just-In-Time (JIT) compilation mode in PHP presents an opportunity for differential testing, as it provides an alternative implementation of the same language specification. We propose, ZendDiff, an automatic differential testing framework that effectively detects logic bugs in the PHP interpreter by comparing JIT and non-JIT execution results. Our differential testing incorporates three techniques: program state probing for fine-grained execution state comparison, JIT-aware program mutation to sufficiently exercise JIT functionality, and dual verification to handle non-deterministic behaviors in PHP programs. Our experimental results demonstrate that ZendDiffoutperforms the official test suite used in PHP’s continuous integration, achieving higher code coverage and executing more Zend opcodes. Through ablation studies, we validate the effectiveness of these techniques. To date, ZendDiffhas identified 51 previously unknown logic bugs in the PHP interpreter, with 37 already fixed and 3 confirmed by the PHP maintainers. ZendDiffhas been acknowledged by the PHP community and offers a practical tool for automatically discovering logic bugs in the PHP interpreter.