Large Language Model (LLM) agent frameworks such as LangChain, LlamaIndex, and CrewAI have become critical infrastructure powering production AI systems, yet they remain severely under-tested due to fundamental challenges in automated testing. Unlike traditional software, where crashes serve as reliable oracles, defects in these pure Python frameworks manifest as ordinary exceptions or silent semantic failures, creating profound oracle ambiguity. This problem is exacerbated by strict type governance through Pydantic schemas and complex protocol requirements that cause existing fuzzers to generate overwhelming invalid inputs, while traditional test generators produce only trivial cases with weak regression assertions. We present LogicHunter, a fuzzing framework that addresses both the generation and oracle challenges through active specification-aware testing. LogicHunter employs specification-driven generation that systematically fuses formal type constraints with authentic usage patterns from real-world repositories, synthesizing inputs that are valid by construction yet semantically extreme, equipped with behavioral probes to expose silent failures. To resolve oracle ambiguity, we introduce the Agentic Oracle, which transcends passive classification by actively retrieving documentation, navigating source code, and inspecting runtime states through a ReAct-based architecture with Dual-Layer State Management and Dual-Stream Memory. Evaluated on three widely deployed frameworks, LogicHunter discovered 40 previously unknown bugs with 30 confirmed and 26 fixed by developers, while state-of-the-art baselines reported no bugs as final findings. The Agentic Oracle achieves 91.17% precision, surpassing the best passive approach at 29.27% by 61 percentage points.
The widespread adoption of open source software (OSS) has introduced significant security risks, with malicious code poisoning attacks increasingly targeting public package registries and open-source platforms. Existing detection approaches, including heuristic-, learning-, and LLM-based methods, suffer from language-specific designs, limited generalization, and high analysis costs, making them unsuitable for large-scale multi-language analysis. To address these challenges, we propose MalTotal, a scalable and cost-effective framework for language-agnostic malicious code detection. MalTotal leverages LLM-assisted semantic reasoning to identify sensitive APIs, perform hybrid semantic slicing, and reconstruct malicious behavior contexts while reducing analysis overhead. Our evaluations show that MalTotal outperforms 8 state-of-the-art baselines, achieving an average F1-score of 93.1% across 5 mainstream languages. Its hybrid slicing reduces LLM token consumption by 94.0%, lowering the analysis cost from \$86.25 to \$5.19 on 2,168 repositories. In a large-scale study of 120K GitHub repositories containing over 7.3 million files, MalTotal discovered 564 previously unknown malicious repositories across multiple languages at a total cost of \$338. These results demonstrate the effectiveness, scalability, and cost-efficiency of MalTotal in mitigating large-scale code poisoning attacks.
Jian Zhao, Shenao Wang, Qingyang Wu et al.· 0 citations
Ask a large language model (LLM) whether a pointer dereference is safe, and it can often produce a plausible justification for ``yes''. The difficulty is that a fluent justification is not a proof. This gap is precisely where automated vulnerability detection lives: deciding, for a given operation in source code, whether a memory safety defect such as a null dereference, use-after-free, or double free can actually occur. We trace the unreliability of LLM-based vulnerability detection to a mechanism, the premature discharge of safety obligations, and argue that the remedy is not better prompting but a separation of roles: the component that interprets the code must not also be the one that decides a safety obligation is met. In this paper, we present LeanGuard, a neuro-symbolic framework that assigns each act to the side equipped for it. On the neural side, an LLM serves strictly as a semantic filter over candidate facts extracted from the abstract syntax tree (AST): it prunes spurious facts and keeps the real ones, but never discharges an obligation or decides the verdict on its own. On the symbolic side, the surviving facts are compiled into a verification model in Lean 4 (a formal proof assistant whose kernel accepts a conclusion only when it is formally proved), where every dangerous operation must be matched by a guard that provably covers it in scope; absent such a guard, the obligation stays open rather than being argued away. Because a function rarely arrives with full context, this symbolic model is necessarily partial: an unproved obligation is not yet a defect. An evidence-aware adjudicator therefore weighs the symbolic and neural verdicts by the quality of each. We instantiate the framework on five CWE classes to ask how far this division of labor can be pushed.
Yanjie Zhao, Hongjie Chen, Li Lu et al.· 0 citations
IAL-Scan is proposed, a static analysis tool for detecting IAL failures in real-world LLM agent projects, and builds an Agentic Loop Dependence Graph (ALDG) to recover explicit and framework induced feedback paths, and checks whether these paths can repeatedly reach costly or state growing operations without an effective bound.
Xinyi Hou, Shenao Wang, Yanjie Zhao et al.· 2 citations
The evaluation shows that AgentFlow recovers richer agent entities and dependencies than existing AST-based agent static analysis tools, generates more dependency-aware Agent BOMs, and uncovers 238 taint-style prompt-to-tool risks in real-world agent programs.
Shenao Wang, Xinyi Hou, Yanjie Zhao et al.· 0 citations