AdversarialCoT: Single-Document Retrieval Poisoning for LLM Reasoning

generation (RAG) enhances large language model (LLM) reasoning by retrieving external documents, but also opens up new attack surfaces. We study knowledge-base poisoning attacks in RAG, where an attacker

InceptionRAG: Stealthy Poisoning Attack Against Retrieval-Augmented Generation

augmented generation (RAG) systems enhance large language models (LLMs) with external knowledge but have been demonstrated to be vulnerable to corpus poisoning. Existing poisoning attacks against RAG largely focus

Provable Watermarking for Data Poisoning Attacks

poisoning-concurrent watermarking, the watermarked poisoning dataset provably ensures both watermarking detectability and poisoning utility, certifying the practicality of watermarking under data poisoning attacks. We validate our theoretical findings through

NameTrap: Steering AI Coding Agents Toward Security Vulnerabilities via Variable Names

agents, reaching 75.0\% for certain agent--model configurations, and substantially outperforms random renaming, neutral renaming, and the prior identifier-poisoning attack XOXO. The attack remains effective under Traditional Chinese prompts

Decoding Deception: Understanding Automatic Speech Recognition Vulnerabilities in Evasion and Poisoning Attacks

Optimization. Further, the novelty of the paper includes how poisoning attack can degrade the performances of state-of-the-art models leading to misinterpretation of audio signals. Through experimentation

VENOMREC: Cross-Modal Interactive Poisoning for Targeted Promotion in Multimodal LLM Recommender Systems

language models (MLLMs) are pushing recommender systems (RecSys) toward content-grounded retrieval and ranking via cross-modal fusion. We find that while cross-modal consensus often mitigates conventional poisoning that

Graph-Aware Text-Only Backdoor Poisoning for Text-Attributed Graphs

platforms, an attacker may be able to quietly poison a small part of the training data and later make the model produce wrong predictions on demand. This paper studies that

Pmeta-TLA: Backdoor Attacks for Speech Classification Models via Meta-Learning with Timbre Leakage Attack

tool within it. We performed tests on data-poisoning backdoor attacks in keyword spotting tasks utilizing some deep neural network models. Experimental results indicate that the proposed strategy attains superior

Governance Architecture for Autonomous Agent Systems: Threats, Framework, and Engineering Practice

Autonomous agents powered by large language models introduce a class of execution-layer vulnerabilities -- prompt injection, retrieval poisoning, and uncontrolled tool invocation -- that existing guardrails fail to address systematically

ARBITRA: Robust Document-Level ATT&CK Extraction via Multi-Expert Argumentation

conflicting TTP statements, cross-report contamination, and semantic poisoning, ARBITRA limits mean precision degradation to roughly half that of frontier-model baselines for a more robust extraction

Selective Channel Restoration for Backdoored Vision-Language Models

Vision-language models (VLMs) exhibit strong multimodal capabilities but remain vulnerable to backdoors implanted through poisoned fine-tuning data. Existing defenses often require extensive parameter updates during fine-tuning

Backdoor-Powered Prompt Injection Attacks Nullify Defense Methods

prompt injection attack purposes. Specifically, the attackers poison the supervised fine-tuning samples and insert the backdoor into the model. Once the trigger is activated, the backdoored model executes

Low Rank Comes with Low Security: Gradient Assembly Poisoning Attacks against Distributed LoRA-based LLM Systems

separately, while only their product $AB$ determines the model update, yet this composite is never directly verified. We propose Gradient Assembly Poisoning (GAP), a novel attack that exploits this blind

Sentry: Authenticating Machine Learning Artifacts on the Fly

reliance on external datasets and pre-trained models exposes the system to supply chain attacks where an artifact can be poisoned before it is delivered to the end-user. Such

ProtegoFed: Backdoor-Free Federated Instruction Tuning with Interspersed Poisoned Data

Federated Instruction Tuning (FIT) enables collaborative instruction tuning of large language models across multiple organizations (clients) in a cross-silo setting without requiring the sharing of private instructions. Recent findings

ShareLock: A Stealthy Multi-Tool Threshold Poisoning Attack Against MCP

With the rapid evolution of LLM-driven agents, Model Context Protocol (MCP), an open protocol bridging LLMs with external tools, has quickly become foundational to modern agent ecosystems. However

Conjunctive Poisoning in AI Supply-Chain Applications

Large Language and Vision-Language Models are increasingly deployed through

Exposing Citation Vulnerabilities in Generative Engines

generation that cites web pages using large language models. Because anyone can publish information on the web, GEs are vulnerable to poisoning attacks. Existing studies of citation evaluation focus

Unmasking Backdoors: An Explainable Defense via Gradient-Attention Anomaly Scoring for Pre-trained Language Models

behavior of backdoored pre-trained encoder-based language models, focusing on the consistent shift in attention and gradient attribution when processing poisoned inputs; where the trigger token dominates both attention

Open WebUI Vulnerable to Unauthenticated RAG Configuration Disclosure