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Harnessing the Power of LLM to Support Binary Taint Analysis
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This paper proposes LATTE, the first static binary taint analysis that is powered by a large language model (LLM). LATTE is superior to the state of the art (e.g., Emtaint, Arbiter, Karonte) in three aspects. First, LATTE is fully automated while prior static binary taint analyzers need rely on human expertise to manually customize taint propagation rules and vulnerability inspection rules. Second, LATTE is significantly effective in vulnerability detection, demonstrated by our comprehensive evaluations. For example, LATTE has found 37 new bugs in real-world firmware which the baselines failed to find, and 7 of them have been assigned CVE numbers. Lastly, LATTE incurs remarkably low engineering cost, making it a cost-efficient and scalable solution for security researchers and practitioners. We strongly believe that LATTE opens up a new direction to harness the recent advance in LLMs to improve vulnerability analysis for binary programs.
Forward citations
Cited by 9 Pith papers
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Veritas: Grounding LLM Agents for Reliable Vulnerability Reasoning over Stripped Binaries
Veritas detects memory corruption vulnerabilities in stripped binaries by combining static value-flow slicing, dual-view LLM reasoning, and multi-agent runtime validation, reporting 90% recall, zero false positives on...
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Veritas: Grounding LLM Agents for Reliable Vulnerability Reasoning over Stripped Binaries
Veritas detects out-of-bounds vulnerabilities in stripped binaries at 90% recall by grounding LLM reasoning in static witness-backed flows and runtime validation.
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From Craft to Kernel: A Governance-First Execution Architecture and Semantic ISA for Agentic Computers
Arbiter-K is a governance-first architecture that turns probabilistic agent reasoning into discrete instructions with runtime taint propagation to block unsafe actions, reporting 76-95% interception rates and a 92.79%...
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From Craft to Kernel: A Governance-First Execution Architecture and Semantic ISA for Agentic Computers
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The Periodic Table of LLM Reasoning: A Structured Survey of Reasoning Paradigms, Methods, and Failure Modes
A literature survey that introduces a taxonomy for LLM reasoning paradigms, analyzes methodological trends, and synthesizes failure modes from over 300 papers.
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Large Language Models in Cybersecurity: Applications, Vulnerabilities, and Defense Techniques
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