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Prompt Injection Through Forensic Evidence: A Taxonomy and Proof of Concept of Anti-Forensic Attacks on LLM-Assisted Digital Investigations

Frank Breitinger, Jan Gruber, Christopher Hargreaves, Jan-Niclas Hilgert, Gaëtan Michelet, Mark Scanlon · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.29747 · cited by 0 works (OpenAlex) · open-access PDF

Japanese

大規模な言語モデル(LLMs)は、検査アシスタントや自動代理人としてデジタル法医学に入っています。デジタル法医学は、間接的スピードインジェクションの通常の設定を逆転させます:分析中の証拠は、定義によって攻撃者によって制御され、検査者はそれを密接に読む必要があります。これは、データとツールの古典的なものに加えて、検査アシスタントとしての新しい標的になります。古典的な反法医学のタクソノミーはそれをカバーしません、およびスピードインジェクションの防御はそれを予測しません。我々は、反法医学のモデルを論理の操作を反映するように拡張し、反法医学的な証拠を通じて行われる攻撃のタク

Machine translation (Meta M2M100); the English original follows.

Abstract

Large language models (LLMs) are entering digital forensics as examination assistants and autonomous agents. Digital forensics inverts the usual setting for indirect prompt injection: the evidence under analysis is attacker-controlled by definition, and the examiner is obliged to read it closely. This makes the reasoning layer a new anti-forensic target, next to the classical ones of data and tools. Classical anti-forensic taxonomies do not cover it, and prompt injection defences do not anticipate it. We extend the anti-forensic model to reflect the manipulation of reasoning, and present a taxonomy of attacks carried through adversarial evidence: an attacker selects an objective, a technique, a payload location, and a concealment method. We observe a perception gap, i.e., the fact that deterministic tools, the human examiner, and the model perceive the same evidence differently, which is what lets an injection reach the model unseen by the examiner. We demonstrate three attacks against an LLM-assisted analysis pipeline, changing a reported conclusion, writing attacker-chosen content to the analyst's workstation, and causing outbound network traffic, each driven only by text planted on the seized device. Mitigations to these attacks are also proposed.

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Topics: Computer science, Adversarial system, Taxonomy (biology), Computer security, Digital evidence, Digital forensics

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