Impact

Data privacy Vulnerabilities

Vulnerabilities compromising user or training data privacy

54 matching entries out of 102 in this category

Data privacy research results

54 entries

Arbitrary Agent Topology Breach

Updated 4/10/2026

A vulnerability in LLM-based Multi-Agent Systems (LLM-MAS) allows an attacker who controls a single arbitrary agent to map and extract the system's entire confidential communication topology. Unlike prior attacks that rely on direct identity queries and administrative privileges, this attack infers topology stealthily purely from contextual and linguistic signals (stylometry, role-specific syntax), bypassing standard keyword-based and identity-filtering defenses. The exploit relies on a…

WebWeaver: Breaking Topology Confidentiality in LLM Multi-Agent Systems with Stealthy Context-Based Inference

Affects: Llama 3 70B, Llama 3.1 8B, Mistral Large 12B +3 more

Source: arXiv

Adaptive Web Agent Prompt Injection

Updated 2/21/2026

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

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

Source: arXiv

Agent Tool-Call Safety Gap

Updated 3/8/2026

LLM agents with tool-calling capabilities are vulnerable to a text-action modality divergence (termed the "GAP" vulnerability), where text-level safety alignment fails to transfer to tool-call execution. Attackers can craft adversarial prompts that cause the model to generate a text-based refusal (demonstrating text safety) while simultaneously executing the requested forbidden action through available external tools. Because text generation and tool-call selection operate through partially…

Mind the GAP: Text Safety Does Not Transfer to Tool-Call Safety in LLM Agents

Affects: Claude Sonnet 4.5, GPT-5.2, Grok 4.1 Fast +3 more

Source: arXiv

Automated Stealth Skill Injection

Updated 2/21/2026

A vulnerability exists in LLM-based coding agents that implement modular capability extensions (often referred to as "Agent Skills") where the agent dynamically loads and executes user-provided skill packages. The vulnerability allows for Skill-Based Prompt Injection, specifically leveraging a technique known as "SkillJect." This attack decouples the malicious intent from the operational payload to bypass semantic safety filters. An attacker constructs a skill package containing: 1. Inducement…

SkillJect: Effectively Automating Skill-Based Prompt Injection for Skill-Enabled Agents

Affects: Claude Sonnet 4.6, GPT-5 Mini, GLM-4.7 +7 more

Source: arXiv

Evolutionary Hidden Knowledge Recovery

Updated 2/21/2026

Large Language Models (LLMs) subjected to machine unlearning techniques (specifically AltPO, GradDiff, IDKDPO, IDKNLL, UNDIAL, NPO, and SimNPO) contain a vulnerability regarding the persistence of latent knowledge. Despite achieving high "forgetting" scores on standard, benign benchmarks, these models remain susceptible to black-box evolutionary adversarial attacks. An attacker can utilize an automated framework (REBEL) comprising a "Hacker" model and a "Judge" model to iteratively mutate…

REBEL: Hidden Knowledge Recovery via Evolutionary-Based Evaluation Loop

Source: arXiv

Long-Horizon Agent Attacks

Updated 4/10/2026

LLM agents equipped with tool-use, persistent memory, and environmental interaction capabilities are vulnerable to long-horizon attacks. Attackers can bypass single-turn safety guardrails by exploiting the temporal dimension of multi-turn interactions to incrementally steer agent behavior. The vulnerability manifests because the agent's safety mechanisms perform localized, single-step evaluations but fail to maintain semantic safety across extended interaction trajectories. This enables…

AgentLAB: Benchmarking LLM Agents against Long-Horizon Attacks

Affects: GPT-4o, GPT-5.1, Gemini 3 Flash +1 more

Source: arXiv

Mobile Agent Visual Spoofing

Updated 2/22/2026

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

Source: arXiv

RAG Knowledgebase Exfiltration

Updated 2/21/2026

Retrieval-Augmented Generation (RAG) systems are vulnerable to iterative knowledge-extraction attacks designed to reconstruct the underlying private knowledge base. The vulnerability exists due to the decoupled optimization of the retrieval and generation phases. Attackers can craft adversarial queries consisting of two distinct components: an "Information" component (optimized via gradient descent or random sampling to steer embeddings toward specific, diverse regions of the vector space) and…

Benchmarking Knowledge-Extraction Attack and Defense on Retrieval-Augmented Generation

Affects: GPT-4o, Llama 3 8B, Qwen 2.5 7B

Source: arXiv

Tag-Along Agent Jailbreak

Updated 2/21/2026

A vulnerability exists in tool-augmented Large Language Model (LLM) agents characterized as "Tag-Along Attacks," where an unprivileged external user (or adversarial agent) coerces a safety-aligned Operator agent into executing prohibited tool calls. Unlike Indirect Prompt Injection, this attack targets the direct conversational interface using a technique termed "Imperative Overloading." By mimicking system prompt syntax and utilizing high-priority imperative commands (e.g., "Strict adherence…

David vs. Goliath: Verifiable Agent-to-Agent Jailbreaking via Reinforcement Learning

Affects: Qwen 2.5 32B Instruct AWQ, DeepSeek V3.1, Gemini 2.5 Flash +9 more

Source: arXiv

Voice Agent Behavioral Bypass

Updated 2/21/2026

Audio Large Language Models (ALLMs) integrated into voice agent systems for high-stakes domains (banking, IT support, logistics) are vulnerable to multimodal adversarial attacks via spoken interaction. Adversaries can exploit the model's inherent compliance and contextual awareness through multi-turn dialogue to bypass authentication safeguards, escalate privileges (e.g., unauthorized credit limit increases), exfiltrate sensitive Personally Identifiable Information (PII), and poison…

Aegis: Towards Governance, Integrity, and Security of AI Voice Agents

Affects: GPT-4o, GPT-4o Mini, Gemini 1.5 Pro +4 more

Source: arXiv

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