Story · Semantic Scholar
A Formal Framework of Architectural Intent Collapse for Tool-Level Attacks on LLM Agents (Semantic Scholar)
paper · Story page
Formalizes why tool-level attacks keep working: flattening text from different sources into one context erases the boundary between description and instruction. Its intent-separation metric strongly predicts defense effectiveness (r = -0.97) across 25 framework-model combinations.
In plain words
- Researchers explained why attacks keep succeeding when action-taking artificial intelligence systems combine text from many sources.
- Descriptions, user requests, and commands arrive in one block, so the system can lose track of which text should control its behavior.
- They created a score for this separation and tested it across 25 combinations of artificial intelligence systems and supporting software.
- Clearer separation strongly predicted better defenses, suggesting designers should preserve the original purpose attached to each piece of text.
Appeared in
- Poisoned agent memory beats screening, and prompt rules keep failing as boundaries
Aug 25, 2026 · in the sections
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