arXiv subjects

Evangelos Lamprou

Publications and source records attributed to Evangelos Lamprou.

3 recordsLinked to original sources

arXiv2025

A Proof-of-Concept for Explainable Disease Diagnosis Using Large Language Models and Answer Set Programming

Accurate disease prediction is vital for timely intervention, effective treatment, and reducing medical complications. While symbolic AI has been applied in healthcare, its adoption remains limited due to the effort required for constructing high-quality knowledge bases. This work introduces McCoy, a framework that combines Large Language Models (LLMs) with Answer Set Programming (ASP) to overcome this barrier. McCoy orchestrates an LLM to translate medical literature into ASP code, combines it with patient data, and processes it using an ASP solver to arrive at the final diagnosis. This integration yields a robust, interpretable prediction framework that leverages the strengths of both paradigms. Preliminary results show McCoy has strong performance on small-scale disease diagnosis tasks.

cs.AI↗

arXiv2025

Lexo: Eliminating Stealthy Supply-Chain Attacks via LLM-Assisted Program Regeneration

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.

cs.CR↗

arXiv2025

Caruca: Effective and Efficient Specification Mining for Opaque Software Components

A wealth of state-of-the-art systems demonstrate impressive improvements in performance, security, and reliability on programs composed of opaque components, such as Unix shell commands. To reason about commands, these systems require partial specifications. However, creating such specifications is a manual, laborious, and error-prone process, limiting the practicality of these systems. This paper presents Caruca, a system for automatic specification mining for opaque commands. To overcome the challenge of language diversity across commands, Caruca first instruments a large language model to translate a command's user-facing documentation into a structured invocation syntax. Using this representation, Caruca explores the space of syntactically valid command invocations and execution environments. Caruca concretely executes each command-environment pair, interposing at the system-call and filesystem level to extract key command properties such as parallelizability and filesystem pre- and post-conditions. These properties can be exported in multiple specification formats and are immediately usable by existing systems. Applying Caruca across 60 GNU Coreutils, POSIX, and third-party commands across several specification-dependent systems shows that Caruca generates correct specifications for all but one case, completely eliminating manual effort from the process and currently powering the full specifications for a state-of-the-art static analysis tool.

cs.SE↗