KidnapRAG: A Black-Box Attack for Hijacking Reasoning in Agentic Retrieval-Augmented Generation Systems

Retrieval-Augmented Generation (RAG) systems are vulnerable to poisoning attacks that inject malicious documents into the retrieval process to manipulate model outputs. Recent Agentic RAG systems are more robust

Memory Poisoning Attack and Defense on Memory Based LLM-Agents

memory and influence future responses. Recent work demonstrated that the MINJA (Memory Injection Attack) achieves over 95 % injection success rate and 70 % attack success rate under idealized conditions. However

Autonomous LLM Agent Worms: Cross-Platform Propagation, Automated Discovery and Temporal Re-Entry Defense

graph analyzer, traces data flow from file I/O to LLM context injection points and ranks carriers by context injection position without manual analysis. SRPO, our summary-resilient payload optimizer, generates

Open WebUI Affected by an External Model Server (Direct Connections

Conflict-Aware Retriever Editing for Knowledge Injection Attacks on LLM-Based RAG Systems

mislead downstream generation, posing a serious security threat for AI applications. Existing RAG injection attacks mainly rely on manipulating external knowledge bases, such as crafting malicious corpus. However, the synthetic

TFL: Targeted Bit-Flip Attack on Large Language Model

safety and security critical applications, raising concerns about their robustness to model parameter fault injection attacks. Recent studies have shown that bit-flip attacks (BFAs), which exploit computer main memory

Poisoned Playbooks: Demystifying Knowledge Poisoning Effects on AI Security Agents

challenges and AI agents. First, we demonstrate how a crafted single poisoned write-up injected into public-style security knowledge sources which we denote as Poisoned Playbooks, alters the behavior

Fragile Thoughts: How Large Language Models Handle Chain-of-Thought Perturbations

Chain-of-Thought (CoT) prompting has emerged as a foundational technique for eliciting reasoning from Large Language Models (LLMs), yet the robustness of this approach to corruptions in intermediate reasoning

Contagion on the Trading Floor: How Adversarial Signals Spread in Multi-Agent Trading Systems

black-box poisoning attackers that treat an LLM as a post generator and inject budget-constrained, plausibly benign social-media content into the analyst's evidence stream. We define contagion

PraisonAI: Unauthenticated RCE via Jobs API + Approval Bypass

Autonomous OSS Threat Detection via Taxonomy-Aligned LLMs

from sophisticated supply chain attacks including typosquatting, dependency confusion, Trojan Source obfuscation, malicious build injection, and CI/CD pipeline poisoning. Existing detection approaches rely on signature-based tools and rule-based

OI-Bench: An Option Injection Benchmark for Evaluating LLM Susceptibility to Directive Interference

signals such as social cues, framing, and instructions. In this work, we introduce option injection, a benchmarking approach that augments the multiple-choice question answering (MCQA) interface with an additional

npm PraisonAI SandboxExecutor allowedCommands bypass via shell chaining

Mistune Image Directive CSS Injection Vulnerability

The Decomposition Is the Fingerprint: Per-Component Identity for Agent Skills

distance. Our central claim is that keeping the fingerprint as a per-component triple (prompt, code, tools), rather than a single score, is what makes it useful: the triple recovers

npm PraisonAI utility shell safe-command wrapper allowlist bypass via

Routing Ceilings Are Domain-Independent: Structural Prior Injection in Code Security Vulnerability Detection

Large language models (LLMs) exhibit a well-documented gap between

PraisonAI: HTTPApproval dashboard renders tool arguments as raw HTML, allowing

How Reliable Are AI Attackers Against a Fixed Vulnerable Target? A 400-Run Empirical Study of LLM Penetration Testing Consistency

Large language models (LLMs) can autonomously conduct multi-stage cyber

Transient Turn Injection: Exposing Stateless Multi-Turn Vulnerabilities in Large Language Models

workflows, raising the stakes for adversarial robustness and safety. This paper introduces Transient Turn Injection(TTI), a new multi-turn attack technique that systematically exploits stateless moderation by distributing adversarial