mlsanityData to 7 Oct 2026

Investigating Detection and Obfuscation of Prompt Injection Attacks Against Software Reverse Engineering AI Agents

arXiv 2605.30677 ↗First posted 29 May 2026cs.CR cs.AI cs.SEcited by 0

On the map: LLM agents and tool use → Agent threats, guardrails and governance

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Abstract (arXiv)

Agentic software reverse engineering systems are vulnerable to prompt injection attacks placed into the source code of executable binary files. This research demonstrates defensive tactics for detecting the presences of prompt injection strings in the decompiler output of adversarial example programs. Methods for obfuscating these attacks and subsequent methods for defending against these obfuscations are also explored. This research advances the understanding of risk and security of agentic software analysis systems necessary for their deployment into production-level cyber workflows.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Agent threats, guardrails and governance, one of 16 directions in LLM agents and tool use. Builds on 8 of its 19 references that are in the map. First posted on arXiv 29 May 2026.

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8 of 19 references.

Closest ancestors · 1

Same direction, newest first

  1. 2025PromptArmor: Simple yet Effective Prompt Injection Defenses21 Jul

Other references · 7

  1. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
  2. 2025Qwen3 Technical Reportcited by 8,195
  3. 2023AutoDAN: Generating Stealthy Jailbreak Prompts on Aligned Large Language Modelscited by 738
  4. 2023Prompt Injection attack against LLM-integrated Applicationscited by 568
  5. 2019To Tune or Not to Tune? Adapting Pretrained Representations to Diverse Taskscited by 167
  6. 2025A Critical Evaluation of Defenses against Prompt Injection Attackscited by 31
  7. 2025Fine-Tuning Causal LLMs for Text Classification: Embedding-Based vs. Instruction-Based Approachescited by 5

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  1. 2026Automatically Attacking Software Reverse Engineering AI Agents
  2. 2026Prompt Injection Attacks on Agentic Coding Assistants: A Systematic Analysis of Vulnerabilities in Skills, Tools, and Protocol Ecosystems
  3. 2025"Your AI, My Shell": Demystifying Prompt Injection Attacks on Agentic AI Coding Editors
  4. 2026Challenges and Future Directions in Agentic Reverse Engineering Systems
  5. 2025AVIATOR: Towards AI-Agentic Vulnerability Injection Workflow for High-Fidelity, Large-Scale Code Security Dataset
  6. 2025Analyzing Code Injection Attacks on LLM-based Multi-Agent Systems in Software Development
  7. 2026The Next Challenge for Agentic Cybersecurity: A Realistic, Contamination-Free Reverse Engineering Benchmark
  8. 2026Skill-Inject: Measuring Agent Vulnerability to Skill File Attacks