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| Hauptverfasser: | , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| Schlagworte: | |
| Online-Zugang: | https://arxiv.org/abs/2510.14522 |
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| _version_ | 1866912766660444160 |
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| author | Lamprou, Evangelos Dai, Julian Ntousakis, Grigoris Rinard, Martin C. Vasilakis, Nikos |
| author_facet | Lamprou, Evangelos Dai, Julian Ntousakis, Grigoris Rinard, Martin C. Vasilakis, Nikos |
| contents | Software supply-chain attacks are an important and ongoing concern in the open source software ecosystem. These attacks maintain the standard functionality that a component implements, but additionally hide malicious functionality activated only when the component reaches its target environment. Lexo addresses such stealthy attacks by automatically learning and regenerating vulnerability-free versions of potentially malicious components. Lexo first generates a set of input-output pairs to model a component's full observable behavior, which it then uses to synthesize a new version of the original component. The new component implements the original functionality but avoids stealthy malicious behavior. Throughout this regeneration process, Lexo consults several distinct instances of Large Language Models (LLMs), uses correctness and coverage metrics to shepherd these instances, and guardrails their results. An evaluation on 100+ real-world packages, including high-profile stealthy supply-chain attacks, indicates that Lexo scales across multiple domains, regenerates code efficiently (<30m on average), maintains compatibility, and succeeds in eliminating malicious code in several real-world supply-chain-attacks, even in cases when a state-of-the-art LLM fails to eliminate malicious code when given the source code of the component and prompted to do so. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_14522 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Lexo: Eliminating Stealthy Supply-Chain Attacks via LLM-Assisted Program Regeneration Lamprou, Evangelos Dai, Julian Ntousakis, Grigoris Rinard, Martin C. Vasilakis, Nikos Cryptography and Security Software supply-chain attacks are an important and ongoing concern in the open source software ecosystem. These attacks maintain the standard functionality that a component implements, but additionally hide malicious functionality activated only when the component reaches its target environment. Lexo addresses such stealthy attacks by automatically learning and regenerating vulnerability-free versions of potentially malicious components. Lexo first generates a set of input-output pairs to model a component's full observable behavior, which it then uses to synthesize a new version of the original component. The new component implements the original functionality but avoids stealthy malicious behavior. Throughout this regeneration process, Lexo consults several distinct instances of Large Language Models (LLMs), uses correctness and coverage metrics to shepherd these instances, and guardrails their results. An evaluation on 100+ real-world packages, including high-profile stealthy supply-chain attacks, indicates that Lexo scales across multiple domains, regenerates code efficiently (<30m on average), maintains compatibility, and succeeds in eliminating malicious code in several real-world supply-chain-attacks, even in cases when a state-of-the-art LLM fails to eliminate malicious code when given the source code of the component and prompted to do so. |
| title | Lexo: Eliminating Stealthy Supply-Chain Attacks via LLM-Assisted Program Regeneration |
| topic | Cryptography and Security |
| url | https://arxiv.org/abs/2510.14522 |