CHAINTRIX: A multi-pipeline LLM-augmented framework for automated smart-contract security auditing

Researchers introduce Chaintrix, an LLM-augmented smart-contract auditing framework that combines AI analysis with deterministic structural verification to reduce false positives. The system achieves 71.7% recall on high-severity vulnerabilities, outperforming existing AI and static analysis tools by 26 percentage points on OpenAI's EVMbench benchmark.

Smart-contract security remains a critical bottleneck in blockchain adoption, with automated auditing tools struggling to balance detection accuracy against false-positive rates. Chaintrix addresses this fundamental tension by implementing a multi-layered validation architecture where every LLM-generated security finding must be verified against a deterministic structural representation of the code. This approach acknowledges a key weakness in current AI-driven security tools: language models excel at pattern recognition but frequently generate plausible-sounding but incorrect vulnerability claims that fail real-world triage.

The framework's innovation centers on the Cross-Contract Interaction Model, which parses Solidity code into a machine-readable map of function interactions, state modifications, and external calls. This structured representation serves as ground truth against which both deterministic analysis engines and LLM pipelines operate. By funneling findings through a Structural Verdict Engine that applies deterministic checks before symbolic execution and fuzzing, Chaintrix significantly reduces hallucination-induced false positives while maintaining high detection rates.

The 71.7% recall rate on high-severity vulnerabilities represents meaningful progress for automated tooling, particularly the 25 audits achieving perfect recall. For developers and security teams, this suggests a practical path toward supplementing manual audits with AI-assisted tools that maintain acceptable confidence levels. The architecture's emphasis on deterministic verification rather than pure LLM inference hints at the future direction of AI security tools—hybrid systems that leverage model capabilities while grounding outputs in verifiable code analysis. As smart-contract value continues growing, improving audit efficiency without sacrificing accuracy directly impacts ecosystem security and user fund protection.

• →Chaintrix combines LLM analysis with deterministic structural verification, reducing false positives inherent in pure AI or static analysis approaches.
• →The framework achieves 71.7% recall on high-severity vulnerabilities, outperforming comparable tools by 26 percentage points on EVMbench.
• →A Cross-Contract Interaction Model parses Solidity into structured code representation, serving as ground truth for verification pipelines.
• →Multi-stage filtering with symbolic execution and fuzz testing validates high-confidence findings, improving practical usability for security teams.
• →Results suggest hybrid AI-deterministic systems represent the next generation of smart-contract security tooling.