Attack Context

AI agents Research

Security research involving AI agents and autonomous systems

20 matching entries out of 267 in this category

AI agents research results

20 entries

Agentic Robot Instruction Attack

Published Mar 26, 2026Analyzed Apr 10, 2026Source: arXiv

Vision-Language-Action (VLA) models are vulnerable to targeted, low-budget textual perturbations in their natural-language instruction inputs, which can maliciously alter sequential decision-making and downstream physical robotic behavior. Because VLA policies tightly couple language, perception, and control, bounded edits—such as character-level typos, token attribute swaps, or prompt-level uncertainty clauses—propagate through the model's execution trajectory. This allows a black-box…

SABER: A Stealthy Agentic Black-Box Attack Framework for Vision-Language-Action Models

Evaluated models: Not reported

Mobile Agent Visual Spoofing

Published Feb 11, 2026Analyzed Feb 22, 2026Source: arXiv

Mobile Large Language Model (LLM) agents operating under the "Screen-as-Interface" paradigm are vulnerable to visual indirect prompt injection and state desynchronization. Agents that rely on unstructured visual data (screenshots) and Accessibility Service APIs to perceive the environment lack a mechanism to distinguish between trusted system UI elements and untrusted content (e.g., web pages, emails, or malicious overlays). An attacker can inject visual cues, fake notifications, or hidden…

Blind Gods and Broken Screens: Architecting a Secure, Intent-Centric Mobile Agent Operating System

Evaluated models: Not reported

Adaptive Web Agent Prompt Injection

Published Feb 9, 2026Analyzed Feb 21, 2026Source: arXiv

Large Language Model (LLM) based web agents (such as those built using the BrowserUse scaffold) are vulnerable to Indirect Prompt Injection (IPI) attacks when autonomously navigating and processing untrusted web content. Unlike standard Cross-Site Scripting (XSS), this vulnerability occurs when the LLM orchestrator consumes the DOM or visual screenshots of a webpage containing concealed or contextually disguised adversarial instructions. The LLM interprets these embedded text strings as…

MUZZLE: Adaptive Agentic Red-Teaming of Web Agents Against Indirect Prompt Injection Attacks

Evaluated models: GPT-4.1, GPT-4o, Qwen3-VL 32B Instruct

Visual Exclusivity Agentic Jailbreak

Published Feb 5, 2026Analyzed Apr 10, 2026Source: arXiv

Frontier Multimodal Large Language Models (MLLMs) are vulnerable to Visual Exclusivity (VE) attacks, an "Image-as-Basis" threat where malicious intent is achieved through joint reasoning over benign text and complex technical visual content (e.g., blueprints, schematics, network diagrams). Unlike wrapper-based attacks that conceal malicious text via typography or adversarial noise, VE exploits the model's core visual reasoning capabilities. Attackers can bypass safety filters by combining…

Visual Exclusivity Attacks: Automatic Multimodal Red Teaming via Agentic Planning

Evaluated models: Llama 3.2 11B Vision, InternVL3 8B, Qwen3-VL 8B+5 more

Llama 3.2 11B Vision
InternVL3 8B
Qwen3-VL 8B
GPT-4o
GPT-5
Claude 3.7 Sonnet
Claude Sonnet 4.5
Gemini 2.5 Pro

Visual Object Injection

Published Jan 24, 2026Analyzed Feb 22, 2026Source: arXiv

Large Vision-Language Models (LVLMs) are vulnerable to Physical Prompt Injection Attacks (PPIA), a query-agnostic injection technique delivered via the visual modality. The vulnerability stems from the model's "Vision-Enabled Text Recognition" capabilities and "Identity Sensitivity," where the model interprets text embedded in the physical environment (e.g., printed on signs, posters, or objects) as high-priority instructions rather than passive visual data. An attacker can embed adversarial…

Physical Prompt Injection Attacks on Large Vision-Language Models

Evaluated models: GPT-4o, GPT-4o Mini, GPT-4 Turbo+7 more

GPT-4o
GPT-4o Mini
GPT-4 Turbo
Gemini 1.5 Pro Latest
Gemini 1.5 Pro 002
Gemini 1.5 Flash Latest
Claude 3.5 Sonnet Latest
Claude 3.5 Haiku 20241022
Llama 3.2 11B Vision
Llama 3.2 90B Vision Instruct

Dual Stego MLLM Jailbreak

Published Dec 23, 2025Analyzed Dec 30, 2025Source: arXiv

Commercial Multimodal Large Language Model (MLLM) integrated systems are vulnerable to a "Dual Steganography" jailbreak paradigm (referred to as Odysseus). The vulnerability arises from the reliance of safety filters on the assumption that malicious content must be explicitly visible in the input or output modalities (text or image). Attackers can bypass these filters by encoding malicious queries into binary matrices and embedding them into benign-looking images using steganographic encoders…

Odysseus: Jailbreaking Commercial Multimodal LLM-integrated Systems via Dual Steganography

Evaluated models: GPT-4o, Gemini 2.0 Pro, Gemini 2.0 Flash+1 more

GPT-4o
Gemini 2.0 Pro
Gemini 2.0 Flash
Grok 3

Indirect Environmental Jailbreak

Published Nov 20, 2025Analyzed Dec 30, 2025Source: arXiv

Embodied Artificial Intelligence (AI) agents utilizing Vision-Language Models (VLMs) for perception and planning are vulnerable to Indirect Environmental Jailbreak (IEJ). The vulnerability arises from the system's failure to distinguish between user-issued instructions and text embedded in the physical environment (e.g., writing on walls, sticky notes, or projections). The VLM processes visual text detected in the camera feed as authoritative context or direct commands, allowing a black-box…

The Shawshank Redemption of Embodied AI: Understanding and Benchmarking Indirect Environmental Jailbreaks

Evaluated models: GPT-4o, Qwen3-VL Plus, Gemini 2.0 Flash+3 more

GPT-4o
Qwen3-VL Plus
Gemini 2.0 Flash
GLM 4.5
DeepSeek-VL2
Claude 3.5 Sonnet

Black-Box Confidence Exploit

Published Oct 19, 2025Analyzed Dec 8, 2025Source: arXiv

A vulnerability exists in the self-reflection and introspection capabilities of Large Language Models (LLMs) and Vision-LLMs that allows attackers to perform black-box adversarial optimization using only textual model responses. This technique, termed "Asking for Directions" (AfD), bypasses the need for access to gradients, logits, or continuous confidence scores. The attacker employs a hill-climbing optimization strategy where they present the target model with two candidate inputs (an…

Black-box Optimization of LLM Outputs by Asking for Directions

Evaluated models: Qwen 2.5 VL 3B Instruct, Qwen 2.5 VL 7B Instruct, Qwen 2.5 VL 72B Instruct+8 more

Qwen 2.5 VL 3B Instruct
Qwen 2.5 VL 7B Instruct
Qwen 2.5 VL 72B Instruct
Llama 3.2 11B Vision
Llama 3.2 90B Vision
Llama 3.1 70B Instruct
GPT-4o
GPT-4o Mini
GPT-5 Mini
Claude 3.5 Haiku
Claude 3.7 Sonnet

Touch-Guided Mobile Agent Jailbreak

Published Oct 9, 2025Analyzed Dec 9, 2025Source: arXiv

Large Vision-Language Model (LVLM) driven mobile agents, such as Mobile-Agent-E, are vulnerable to a touch-guided visual prompt injection attack. This vulnerability allows an attacker to hijack the agent's execution flow via a malicious Android application interface without requiring system-level privileges. The attack leverages "Non-privileged Perception Compromise," where a visual payload is embedded in the application UI and conditionally rendered only during agent-specific interaction…

Practical and Stealthy Touch-Guided Jailbreak Attacks on Deployed Mobile Vision-Language Agents

Evaluated models: GPT-4o, Gemini 2.0 Pro Exp 0205, Claude 3.5 Sonnet+3 more

GPT-4o
Gemini 2.0 Pro Exp 0205
Claude 3.5 Sonnet
Qwen VL Max
DeepSeek-VL2
LLaVA-OneVision

LLM Self-Evolving Safety Decline

Published Sep 30, 2025Analyzed Dec 30, 2025Source: arXiv

Large Language Models (LLMs), including proprietary and open-weight state-of-the-art systems, are vulnerable to automated, self-evolving adversarial attacks orchestrated by multi-agent frameworks. The vulnerability exists because current safety alignment strategies (RLHF, static safety filters) fail to generalize against the "SafeEvalAgent" attack vector. In this vector, an "Analyst" agent analyzes model refusals to iteratively refine attack strategies, while a "Specialist" agent grounds these…

SafeEvalAgent: Toward Agentic and Self-Evolving Safety Evaluation of LLMs

Evaluated models: GPT-5, GPT-5 Chat Latest, Gemini 2.5 Pro+7 more

GPT-5
GPT-5 Chat Latest
Gemini 2.5 Pro
Gemini 2.5 Flash
Grok 4
Qwen 3 8B
Qwen 3 32B
Llama 4 Scout
Llama 4 Maverick
DeepSeek V3.1

Related research categories