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ARXIV:2604.19844 · EMBODIED AI SECURITY · SUBMITTED 23 APR · 05:08 UTC · FRESHNESS STALE
ARXIV:2604.19844EMBODIED AI SECURITYSUBMITTED 23 APR · 05:08 UTCFRESHNESS STALEJiamin Chang · Minhui Xue · Ruoxi Sun · Shuchao Pang · Salil S. Kanhere · Hammond Pearce · arXiv
A defense framework for embodied AI agents to distinguish between legitimate environmental signals and malicious visual injections.
Opportunity summary
Pain A defense framework for embodied AI agents to distinguish between legitimate environmental signals and malicious visual injections.
Evidence 0 refs | 3 sources | 50% coverage
Blocker Evidence unverified
A defense framework for embodied AI agents to distinguish between legitimate environmental signals and malicious visual injections. Within this context, environmental signals such as traffic lights are essential in-band signals that can and should…
Recent advances in embodied Vision-Language Agentic Systems (VLAS), powered by large vision-language models (LVLMs), enable AI systems to perceive and reason over real-world scenes. Within this context, environmental signals such as traffic lights are…
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Recent advances in embodied Vision-Language Agentic Systems (VLAS), powered by large vision-language models (LVLMs), enable AI systems to perceive and reason over real-world scenes.…
Embodied AI Security moved forward this cycle; last verified April 2026. Public score 7.0/10. Production flags indicate code availability.
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A defense framework for embodied AI agents to distinguish between legitimate environmental signals and malicious visual injections.
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10.48550/arXiv.2604.19844A defense framework for embodied AI agents to distinguish between legitimate environmental signals and malicious visual injections.
Abstract
Recent advances in embodied Vision-Language Agentic Systems (VLAS), powered by large vision-language models (LVLMs), enable AI systems to perceive and reason over real-world scenes. Within this context, environmental signals such as traffic lights are essential in-band signals that can and should influence agent behavior. However, similar signals could also be crafted to operate as misleading visual injections, overriding user intent and posing security risks. This duality creates a fundamental challenge: agents must respond to legitimate environmental cues while remaining robust to misleading ones. We refer to this tension as trust boundary confusion. To study this behavior, we design a dual-intent dataset and evaluation framework, through which we show that current LVLM-based agents fail to reliably balance this trade-off, either ignoring useful signals or following harmful ones. We systematically evaluate 7 LVLM agents across multiple embodied settings under both structure-based and noise-based visual injections. To address these vulnerabilities, we propose a multi-agent defense framework that separates perception from decision-making to dynamically assess the reliability of visual inputs. Our approach significantly reduces misleading behaviors while preserving correct responses and provides robustness guarantees under adversarial perturbations. The code of the evaluation framework and artifacts are made available at https://anonymous.4open.science/r/Visual-Prompt-Inject.
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Proof status
unverified0 refs; 3 sources; 50% coverage.
What was readable
Derived fallback: Estimated from adjacent evidence; not verified from source.
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Dimensions overall score 7.0
PROBLEM
A defense framework for embodied AI agents to distinguish between legitimate environmental signals and malicious visual injections. Within this context, environmental signals such as traffic lights are essential in-band signals that can and should influence agent behavior.
METHOD
Recent advances in embodied Vision-Language Agentic Systems (VLAS), powered by large vision-language models (LVLMs), enable AI systems to perceive and reason over real-world scenes. Within this context, environmental signals such as traffic lights are essential in-band signals t...
RESULT
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Recent advances in embodied Vision-Language Agentic Systems (VLAS), powered by large vision-language models (LVLMs), enable AI systems to perceive and reason over real-world scenes. Code availability is f...
WHY NOW
Embodied AI Security moved forward this cycle; last verified April 2026. Public score 7.0/10. Production flags indicate code availability.
{"file name": "input.pdf", "number of pages": 13, "author": "Jiamin Chang; Minhui Xue; Ruoxi Sun; Shuchao Pang; Salil S. Kanhere; Hammond Pearce", "title": "If you're waiting for a sign..
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A defense framework for embodied AI agents to distinguish between legitimate environmental signals and malicious visual injections.
Segment
Embodied AI Security
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Commercial read
7.0/10 public viability
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reason
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proof status
unverified
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