Reva: Real-Time Vulnerability Detection through Contextual Probing of Large Language Models
REVA uses module threat models, static-analysis findings, and call graphs to screen code with an LLM probe in one pass. On PrimeVul, it matched an agentic detector’s accuracy at a fraction of its reported time and cost.
REVA supplies a function’s LLM with a precomputed module threat model, static-analysis findings, and call-graph information, then uses a trained probe to read internal representations. On PrimeVul’s held-out test set, it reached **60.0% accuracy** in about **one second per function**, matching the reported 59.2% for VulTriage on the same LLM.
For teams screening code from agents, the result suggests a path to fast, context-aware checks between code generation and review. The paper reports REVA was over **250 times faster** and cost less than a tenth as much as VulTriage; its probe also beat the LLM’s direct verdict by 14–17% on the same prompt.
REVA supplies a function’s LLM with a precomputed module threat model, static-analysis findings, and call-graph information, then uses a trained probe to read internal representations. On PrimeVul’s held-out test set, it reached **60.0% accuracy** in about **one second per function**, matching the reported 59.2% for VulTriage on the same LLM. For teams screening code from agents, the result suggests a path to fast, context-aware checks between code generation and review. The paper reports REVA was over **250 times faster** and cost less than a tenth as much as VulTriage; its probe also beat the LLM’s direct verdict by 14–17% on the same prompt. These are results from a held-out benchmark, not a guarantee of finding vulnerabilities in a particular codebase. The material does not establish how the detector performs in a production review pipeline.
This offers a fast, context-aware function-level vulnerability screen that could fit between code generation and human review, with benchmark evidence for accuracy, speed, and cost. It does not establish production performance or repository-scale discovery, so it adds a screening signal rather than evidence that a codebase is secure.