The work introduces and partially evaluates seven cross-domain prompt injection detectors, reporting F1 gains on benchmarks like deepset/prompt-injections and indirect-injection sets via local alignment, stylometry, and fatigue tracking.
Adaptive Attacks Break Defenses Against Indirect Prompt Injection Attacks on LLM Agents
5 Pith papers cite this work. Polarity classification is still indexing.
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Prompt injection defenses create a security-fidelity tradeoff with no model or defense achieving both high security and high fidelity on the SecFid benchmark across 1,168 examples.
GIF introduces a Jacobian-based upper bound on input-output mutual information in LLMs with formal Lean proof and strong empirical recall on injection and leakage benchmarks.
Controlled experiments show adversarial feeds can tip uncertain LLM agent decisions from 5% to 100% alignment with the feed while leaving firmly held defaults unchanged, following a dose-response pattern across multiple models and domains.
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Adversarial Feeds Steer LLM Agent Decisions Against Their Defaults
Controlled experiments show adversarial feeds can tip uncertain LLM agent decisions from 5% to 100% alignment with the feed while leaving firmly held defaults unchanged, following a dose-response pattern across multiple models and domains.