AI Threat Alert
VortexPIA: Indirect Prompt Injection Attack against LLMs for Efficient Extraction of User Privacy
integrated applications? To address this question, we propose \textsc{VortexPIA}, a novel indirect prompt injection attack that induces privacy extraction in LLM-integrated applications under black-box settings. By injecting
Piggybacking on Perception: Stealthy Concurrent Audio Prompt Injections against Multimodal LLM Agents
inevitably contain environmental noise beyond user control. In this paper, we investigate concurrent audio prompt injection attacks targeting multimodal agents. Distinct from traditional acoustic attacks on voice devices, we propose
Securing Large Language Models (LLMs) from Prompt Injection Attacks
increasingly being deployed in real-world applications, but their flexibility exposes them to prompt injection attacks. These attacks leverage the model's instruction-following ability to make it perform malicious
Document-Authored Control-Signal Impersonation: A Low-Cost Indirect Prompt Attack on RAG Safety Boundaries
signal when RAG prompt rendering collapses trusted and untrusted text into the same natural-language channel. We evaluate DACSI across six model settings, prompt-pressure levels, injection baselines, signal taxonomies
Forensic Trajectory Signatures for Agent Memory Poisoning Detection
that real-time blocking is feasible with moderate degradation. The boundary is forensically useful: prompt-injection attacks that bypass memory produce a distinct trajectory (score = 0.541), enabling incident responders
Proactive Hardening of LLM Defenses with HASTE
enhance detection efficacy for prompt-based attack techniques. The framework is agnostic to synthetic data generation methods, and can be generalized to evaluate prompt-injection detection efficacy, with and without
Adaptive Evaluation of Out-of-Band Defenses Against Prompt Injection in LLM Agents
converged on a strategy for defending tool-using LLM agents against indirect prompt injection: rather than training the model to refuse malicious instructions, enforce security outside the model with
Exploiting Web Search Tools of AI Agents for Data Exfiltration
functionality and vulnerability to abuse. As LLMs increasingly interact with external data sources, indirect prompt injection emerges as a critical and evolving attack vector, enabling adversaries to exploit models through
Text Prompt Injection of Vision Language Models
vision language models has significantly raised safety concerns. In this project, we investigate text prompt injection, a simple yet effective method to mislead these models. We developed an algorithm
LivePI: More Realistic Benchmarking of Agents Against Indirect Prompt Injectio
increasingly deployed in local workflows with access to external tools. This creates indirect prompt-injection (IPI) risk: an agent may execute harmful instructions embedded in untrusted inputs such as email
Phishing Email Detection Using Large Language Models
based framework to detect phishing email attacks across multiple attack vectors, including prompt injection, text refinement, and multilingual attacks. We evaluate three frontier LLMs (e.g., GPT-4o, Claude Sonnet
The LLMbda Calculus: AI Agents, Conversations, and Information Flow
prompt, and parses the response as a new term. This calculus faithfully represents planner loops and their vulnerabilities, including the mechanisms by which prompt injection alters subsequent computation. The semantics
The Cognitive Firewall:Securing Browser Based AI Agents Against Indirect Prompt Injection Via Hybrid Edge Cloud Defense
autonomous browser agents exposes a significant attack surface in the form of Indirect Prompt Injection (IPI). Cloud-based defenses can provide strong semantic analysis, but they introduce latency and raise
AgentSentry: Mitigating Indirect Prompt Injection in LLM Agents via Temporal Causal Diagnostics and Context Purification
retrieval systems to autonomously complete complex tasks. However, this design exposes agents to indirect prompt injection (IPI), where attacker-controlled context embedded in tool outputs or retrieved content silently steers
CommandSans: Securing AI Agents with Surgical Precision Prompt Sanitization
access to numerous tools and sensitive data significantly widens the attack surface for indirect prompt injections. Due to the context-dependent nature of attacks, however, current defenses are often
Overcoming the Retrieval Barrier: Indirect Prompt Injection in the Wild for LLM Systems
rely on retrieving information from external corpora. This creates a new attack surface: indirect prompt injection (IPI), where hidden instructions are planted in the corpora and hijack model behavior once
Whispers of Wealth: Red-Teaming Google's Agent Payments Protocol via Prompt Injection
teaming evaluation of AP2 and identify vulnerabilities arising from indirect and direct prompt injection. We introduce two attack techniques, the Branded Whisper Attack and the Vault Whisper Attack which manipulate
CodeSentinel: A Three-Layer Defense Against Indirect Prompt Injection in Code Contexts
code context from repositories, documentation, issue threads, and coding-agent environments, creating an indirect prompt-injection surface where attackers hide instructions in comments, strings, identifiers, or decoy code. We propose
The Landscape of Prompt Injection Threats in LLM Agents: From Taxonomy to Analysis
LLMs) has resulted in a paradigm shift towards autonomous agents, necessitating robust security against Prompt Injection (PI) vulnerabilities where untrusted inputs hijack agent behaviors. This SoK presents a comprehensive overview
Data Leakage Prevention in Agentic Applications via Preemptive Hardening
analyzes prompt templates, tool interfaces, and tool-invocation code to identify leakage-enabling patterns and generate actionable patches. The hardened application is then validated through adversarial prompt injection attacks