Internship - LLM-Assisted Reverse Engineering

il y a 1 semaine

Paris, Île-de-France Quarkslab Temps plein

About Quarkslab

Quarkslab builds cutting-edge cybersecurity solutions used by security-driven companies and institutions around the world. Our QShield product suite focuses on software protection and reverse engineering resistance across desktop, mobile, and embedded platforms.

We're not in the cloud — we build real software, tested on real systems. If you enjoy diving deep into complex technical environments, automating smart test coverage, and owning quality end-to-end, read on.

Job description

Description

Explore how a Large Language Model (LLM) can assist human reverse engineers in understanding compiled binaries (x86/ARM). The goal is to link assembly to semantics, automatically infer behavior, identify key routines, and recognize cryptographic primitives.

During the internship you will work a project with some specific goals and milestones.

  • Reproduce existing research such as "Machine-Language Model for Software Security" (see #bibliography below).
    Build a full analysis pipeline (binary disassembly (Ghidra/IDA/Bninja) pseudo-code embeddings- LLM-based interpretation.
  • Extend previous work by:
  • Adding an interactive assistant (chat-based RE helper).
  • Evaluating the tool on real binaries (malware, compiled open-source tools).
  • Measuring performance and accuracy of semantic inference.

What you will do

During the internship you will work a project with some specific goals and milestones.

  • Reproduce existing research such as "Machine-Language Model for Software Security" (see #bibliography below).
    Build a full analysis pipeline (binary disassembly (Ghidra/IDA/Bninja) pseudo-code embeddings- LLM-based interpretation.
  • Extend previous work by:
  • Adding an interactive assistant (chat-based RE helper).
  • Evaluating the tool on real binaries (malware, compiled open-source tools).
  • Measuring performance and accuracy of semantic inference.

Expected Results

  • A prototype tool that describes binary behavior using an LLM.
  • Quantitative evaluation (accuracy of function descriptions).
  • Qualitative evaluation of usefulness for human analysts.
Profile

Required Skills

  • Programing: Python (intermediate)
  • Reverse engineering (intermediate)
  • Assembly and binary structures(intermediate)
  • Prompt engineering & use of LLM APIs (basic)

Bibliography

  • Zhang Chao et al., Machine-Language Models for Software Security
  • Shang et. al, BinMetric: A Comprehensive Binary Code Analysis Benchmark for Large Language Models.
  • Microsoft Research, CodeXGLUE: A Machine Learning Benchmark Dataset for Code Understanding and Generation.

Assignment

  • Get the apksigner app.
    Build a simple pipeline to decompile analyze LLM- synthesize.
  • In a short document, provide the resulting synthesis and 2 pages explaining how you built the pipeline.

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