Automatically Attacking Software Reverse Engineering AI Agents studies prompt injection through strings embedded in a binary. It matters for any workflow that gives decompiled text to a language-model agent: attacker-controlled target content can be mistaken for an instruction and corrupt the explanation. Use the paper as a threat model, while checking its experimental conditions before generalizing failure rates.
Practical lesson
Keep target strings, comments and decompiler output as lower-trust evidence. Ask an agent to cite addresses and raw observations, and verify conclusions outside the model's narrative.
Evidence scope
The arXiv record is a research report with demonstrated examples. It does not establish that every agent or analysis setup is vulnerable in exactly the same way.
Requirements & evidence
- Resource type
- Paper
- Category
- Unknown
- Host platforms
- Unknown
- Targets
- Unknown
- Level
- Unknown
- Author(s)
- Brian Crawford, Justin Phillips, Patrick McClure
- Year
- 2026
- Venue
- arXiv preprint
- Current version
- v1
- Version date
- 2026-05-28
- Topic
- Adversarial text in binary strings can corrupt an LLM analysis
- Source checked
- 2026-11-04T10:07:15.376059+00:00
Unknown fields have not been established by an author or publisher source.
Primary sources
Source descriptions establish documented scope. They do not establish that every workflow has been reproduced by AI Craft Pad.