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RAISED: Self-Distillation for Robustness to Prompt Injection in LLM Agents
arXiv cs.CL — Computation and Language
Factual evidence
What the source reports
Researchers demonstrate self-distillation improves LLM agent robustness to indirect prompt injection while mitigating capability drift.
Inspect the evidence
- Inclusion basis
- Enterprise AI
- Publisher and source type
- arXiv cs.CL — Computation and Language · RESEARCH
- Published by source
- 8 October 2026
- Collected by OneBench
- 9 Oct 2026, 03:01 UK
Stored source excerpt
arXiv:2610.06401v2 Announce Type: replace-cross Abstract: Tool-using language-model agents are vulnerable to indirect prompt injection because they must act on untrusted external content. Existing training-time defenses…
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OneBench interpretation
Institutional assessment
So what
Prompt injection remains a primary security blocker for deploying agentic AI systems that process untrusted external financial data.
Do what
Review agentic AI safety controls with the team responsible for model security evaluation.