Hallucination as Exploit: Evidence-Carrying Multimodal Agents
Authors: Guijia Zhang, Hao Zheng, Harry Yang
Summary
arXiv:2605. 19192v1 Announce Type: new Abstract: Multimodal agents use screenshots, documents, and webpages to choose tool calls.
Relevance
Read next because Hallucination as Exploit: Evidence-Carrying Multimodal Agents overlaps with clean result "LoRA persona trained on alone emits at 23.5% when a co-trained partner learns ..., 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: text, rect, under, correct, line, rate, implement, extraction. Source: arxiv cs.AI (Artificial Intelligence).
Threat model
Potential threat/caveat for clean result "LoRA persona trained on alone emits at 23.5% when a co-trained partner learns ..., vs 0% control on Qwen2.5-7B-Instruct (MODERATE confidence)": this item discusses failure, benchmark.
Abstract
arXiv:2605.19192v1 Announce Type: new Abstract: Multimodal agents use screenshots, documents, and webpages to choose tool calls. When a false visual claim triggers a click, email, extraction, or transfer, hallucination becomes an authorization failure rather than an answer-quality error. We formalize this failure mode as hallucination-to-action conversion: an unsupported perceptual claim supplies the precondition that makes a privileged action appear permitted. We propose evidence-carrying multimodal agents (ECA), which treat free-form model text as inadmissible evidence. ECA decomposes each tool call into action-critical predicates, obtains typed certificates from constrained DOM/OCR/AX verifiers, and lets a deterministic gate grant only the privileges those certificates support. The architecture does not hide perception error; it converts opaque model belief into named verifier, schema, and implementation residuals. Verifier red-teaming over 1,900 attacks exposes this residual directly: four targeted hardening steps reduce gate bypass from 15% to 1.3%. With content-derived certificates, ECA obtains 0% unsafe-action rate on a 200-task end-to-end pipeline (Wilson 95% upper bound 2.67%) and a 120-task browser proof-of-concept (upper bound 4.3%). A direct HACR audit on 500 stratified task keys shows that unsupported action-critical claims reach unsafe execution for naive agents (100.0%) and prompt-only defense (49.6%), but not for ECA. Oracle-certificate replay on 7,488 GPT-5.4 benchmark traces serves as a gate-correctness sanity check, and neural judge baselines remain bypassable under the same threat model. The resulting principle is simple: model language may propose actions, but external evidence must authorize them.