Iman Sharafaldin | Searcharxiv

arXiv2026

GraphQLer: Enhancing GraphQL Security with Context-Aware API Testing

GraphQL APIs power production systems across financial services, e-commerce, and social platforms, yet their most critical access-control vulnerabilities--Insecure Direct Object Reference (IDOR), Use-After-Free (UAF), and state-dependent injection--routinely escape automated security testing. Industry-standard scanners (ZAP) and the leading research fuzzer (EvoMaster) test operations in isolation and cannot compose the multi-step sequences these flaws require. We present GraphQLer, an open-source automated security testing framework built for production GraphQL APIs. GraphQLer constructs a typed dependency graph from live schema introspection and synthesizes vulnerability chains--ordered operation sequences targeting specific flaw classes. Three strategies cover the critical attack surface: topological SCC-traversal for general reachability, cross-user IDOR replay for broken access control, and CREATE -> DELETE -> READ synthesis for UAF. On a production financial API (FinServ), GraphQLer identified eight potential vulnerabilities--including denial-of-service vectors that exposed stack traces and sensitive implementation details--without prior documentation or authentication credentials. On a self-hosted Saleor instance pinned to the CVE-2022-39275 commit, GraphQLer reproduced all four broken-access-control mutations cited in the security advisory. On the 11 public APIs of the coverage set, GraphQLer achieves 85.52% mean PositiveCoverage versus 29.29% (EvoMaster) and 21.80% (ZAP); across the 21 evaluated APIs it detects all 5 confirmed IDOR endpoints, UAF behavior on two controlled schemas, and confirms XSS and SQLi on DVGA (an independent third-party oracle)--while all baselines detect zero chain-based vulnerabilities.

cs.CR↗

arXiv2025

ZeroFalse: Improving Precision in Static Analysis with LLMs

Static Application Security Testing (SAST) tools are integral to modern software development, yet their adoption is undermined by excessive false positives that weaken developer trust and demand costly manual triage. We present ZeroFalse, a framework that integrates static analysis with large language models (LLMs) to reduce false positives while preserving coverage. ZeroFalse treats static analyzer outputs as structured contracts, enriching them with flow-sensitive traces, contextual evidence, and CWE-specific knowledge before adjudication by an LLM. This design preserves the systematic reach of static analysis while leveraging the reasoning capabilities of LLMs. We evaluate ZeroFalse across both benchmarks and real-world projects using ten state-of-the-art LLMs. Our best-performing models achieve F1-scores of 0.912 on the OWASP Java Benchmark and 0.955 on the OpenVuln dataset, maintaining recall and precision above 90%. Results further show that CWE-specialized prompting consistently outperforms generic prompts, and reasoning-oriented LLMs provide the most reliable precision-recall balance. These findings position ZeroFalse as a practical and scalable approach for enhancing the reliability of SAST and supporting its integration into real-world CI/CD pipelines.

cs.SE↗