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Adaptive Evaluation of Out-of-Band Defenses Against Prompt Injection in LLM Agents

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arXiv:2606. 26479v1 Announce Type: new Abstract: Recent work (2024 to 2026) has converged on a strategy for defending tool-using LLM agents against indirect prompt injection: rather than training the model to refuse malicious instructions, enforce security outside the model with a deterministic policy that mediates the agent's actions.

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Authors: Praneeth Narisetty, Shiva Nagendra Babu Kore, Uday Kumar Reddy Kattamanchi, Jayaram Kumarapu
Released: 2026-06-26 (13d ago)
Topic: Related to current project
Relation to my work: Read next because Adaptive Evaluation of Out-of-Band Defenses Against Prompt Injection in LLM Agents overlaps with clean result "LoRA persona trained on <A> alone emits <B> at 23.5% when a co-trained partner learns <A>...<B>, vs 0% control on Qwen2.5-7B-Instruct (MODERATE confidence)", clean result "Leakage rate is a usable signal for recovering trigger-shaped phrases on Gaperon-1125-1B without knowing the hidden trigger itself (MODERATE confidence)", clean result "Language-mismatch LoRA SFT on Qwen2.5-7B leaks the trained completion language into bystander directives the model was never trained on, absent under same-language SFT (LOW confidence)". Matching terms: strong, class, rect, eval, rate, does, test, qwen2. Source: arxiv cs.CR (Cryptography and Security).
arXiv: 2606.26479

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