Poisoning a single connector in MLLMs establishes a reusable latent backdoor pathway that transfers across modalities with over 95% attack success rate under bounded perturbations.
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(ab) using images and sounds for indirect instruction injection in multi-modal llms
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A unified threat model for LLM-enabled robots reveals three cross-boundary attack chains from user input to unsafe physical actuation due to missing validations and unmediated crossings.
NeuroTaint is the first taint tracking framework for LLM agents that uses offline auditing of semantic, causal, and persistent context to detect flows from untrusted sources to privileged sinks.
Desktop GUI agents face TOCTOU attacks from UI state changes during the ~6.5s observation-to-action gap, with a three-layer pre-execution verification defense achieving 100% interception on two attack types but failing on DOM injection.
AudioHijack generates imperceptible adversarial audio via gradient estimation, attention supervision, and reverberation blending to hijack 13 LALMs with 79-96% success on unseen contexts and real commercial agents.
Relabeling an identical erroneous claim from the model's own thought role to an external chat role increases explicit correction rates by 23-93 percentage points across 13 model-domain cells, indicating a chat-template artifact rather than a cognitive deficit.
Prompt injection vulnerability in tool-augmented LLMs is a model-surface interaction rather than a fixed channel property; the same payload inverts success rates across models, and adaptive attack rate exceeds single-surface baselines by 9.1 pp on average.
Evidence-carrying multimodal agents decompose tool calls into predicates, obtain certificates from DOM/OCR/AX verifiers, and use a deterministic gate to authorize actions only when certificates support them, achieving zero unsafe executions in tested tasks.
Universal adversarial attacks cause output perturbation 90 times more often than precise target injection in VLMs, with only 2 verbatim successes out of 6615 tests.
Injecting brief safety-plausible phrases into robot audio triggers LLM safety halts, enabling semantic denial-of-service attacks where prompt defenses trade attack suppression for impaired genuine hazard detection.
MCP Pitfall Lab operationalizes six pitfall classes across tool-metadata poisoning, puppet servers, and multimodal chains, showing that recommended hardening removes all Tier-1 static findings and that agent narratives mismatch traces in 63% of tested runs.
RCS learns projections on LVLM internal representations to produce contrastive scores that separate malicious jailbreaks from benign inputs, with MCD and KCD variants claiming SOTA generalization to unseen attacks.
RedDiffuser is a reinforced diffusion framework that generates adversarial visual contexts to audit and expose widespread multimodal safety failures in VLMs, increasing unsafe response rates by up to 10.69% on LLaVA with transfer to other models.
Systematic testing of ten LLM agents across 20 tool scenarios and 14 attacks finds universal vulnerability to prompt injection enabling data exfiltration, with tooling amplifying leakage.
Adversarial examples enable AI authority laundering by causing production VLMs to give authoritative but wrong responses on subtly perturbed images, with success rates of 22-100% using decade-old attack methods.
A survey that taxonomizes threats to agentic AI, reviews benchmarks and evaluation methods, discusses technical and governance defenses, and identifies open challenges.
The paper introduces a taxonomy of AI safety for LLMs organized into Trustworthy AI, Responsible AI, and Safe AI perspectives, accompanied by a review of state-of-the-art methods, challenges, and future directions.
A comprehensive survey that taxonomizes safety threats to large models and agents, reviews defenses and benchmarks, and outlines open challenges.
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RedDiffuser: Auditing Multimodal Safety Failures in Vision-Language Models via Reinforced Diffusion
RedDiffuser is a reinforced diffusion framework that generates adversarial visual contexts to audit and expose widespread multimodal safety failures in VLMs, increasing unsafe response rates by up to 10.69% on LLaVA with transfer to other models.