AI Threat AlertAdversaBench: Automated LLM Red-Teaming with Multi-Judge Confirmation and Cross-Model Transferability
real. We present AdversaBench, an end-to-end red-teaming pipeline that mutates seed prompts with five structured operators, queries a target model, and confirms failures through a three-judge
Factual Yet One-Sided: Stance Poisoning and Evidence Balancing in Retrieval-Augmented Generation
knowledge poisoning has primarily targeted factual errors. In contrast, this study focuses on injection attacks in which the facts are correct but the stance on a controversial issue
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
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
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
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
Routing Ceilings Are Domain-Independent: Structural Prior Injection in Code Security Vulnerability Detection
Large language models (LLMs) exhibit a well-documented gap between
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
Cross-Session Threats in AI Agents: Benchmark, Evaluation, and Algorithms
attack taxonomies classified by kill-chain stage and cross-session operation (accumulate, compose, launder, inject_on_reader), each bound to one of seven identity anchors that ground-truth "violation
Clean Data Can Still Carry Backdoors: Support-Persistent Backdoors for Model Reuse
Traditional backdoor attacks inject artificial trigger patterns into training data, causing models to misclassify triggered inputs while maintaining normal behavior on clean samples. Since these artificial triggers are absent
TriFleetRCA: On-Premise LLM Root Cause Analysis for Kubernetes
evidence lines supporting it. We evaluate on a live Kubernetes cluster into which we inject four faults, so ground truth is known by construction, across 100 analyses with Qwen2.5-14B-Instruct
ALIBI: Adversarial Legitimacy Injection in Binary Input against LLM Malware Analyzers
Large language models are being integrated into malware triage workflows