AI Threat AlertTRUSTDESC: Preventing Tool Poisoning in LLM Applications via Trusted Description Generation
injection attack surface: tool poisoning attacks (TPAs). Attackers manipulate tool descriptions by embedding malicious instructions (explicit TPAs) or misleading claims (implicit TPAs) to influence model behavior and tool selection. Existing
ToxicRAG: Compromising Retrieval-Augmented Generation Systems via Single-Shot Knowledge Poisoning Attacks
Augmented Generation (RAG) can ground large language model (LLM) outputs in external evidence, but it also exposes the system to knowledge poisoning. Representative attacks use multiple injected documents or templates
Trustworthy Blockchain-based Federated Learning for Electronic Health Records: Securing Participant Identity with Decentralized Identifiers and Verifiable Credentials
patient data. Despite its potential, FL remains vulnerable to poisoning and Sybil attacks, in which malicious participants corrupt the global model or infiltrate the network using fake identities. While recent
CIBER: A Comprehensive Benchmark for Security Evaluation of Code Interpreter Agents
four major types of adversarial attacks: Direct/Indirect Prompt Injection, Memory Poisoning, and Prompt-based Backdoor. We evaluate six foundation models across two representative code interpreter agents (OpenInterpreter and OpenCodeInterpreter), incorporating
The safety failures we are not instrumenting: a perspective on hidden safety-critical challenges in modern AI systems
legitimacy laundering in retrieval, prompt injection, reward hacking, memory poisoning, evaluation deception, fictional human oversight, synthetic evidence pollution, and model collapse. We conclude with design and governance recommendations
BadSNN: Backdoor Attacks on Spiking Neural Networks via Adversarial Spiking Neuron
converts input data into spikes following the Leaky Integrate-and-Fire (LIF) neuron model. This model includes several important hyperparameters, such as the membrane potential threshold and membrane time constant
Context Contamination in LLM Analysis of Network Security Logs: Poison with Passive Prompt Injection and Mitigation Evaluation
Large Language Models are increasingly deployed in Security Operations Centers
Backdooring Masked Diffusion Language Models
training-time security remains largely unexplored. Existing backdoor attacks on Gaussian diffusion models or autoregressive language models do not directly apply to MDLMs because MDLMs rely on discrete state corruption
PIDP-Attack: Combining Prompt Injection with Database Poisoning Attacks on Retrieval-Augmented Generation Systems
Large Language Models (LLMs) have demonstrated remarkable performance across a wide range of applications. However, their practical deployment is often hindered by issues such as outdated knowledge and the tendency
Human Society-Inspired Approaches to Agentic AI Security: The 4C Framework
software components. Although recent work has strengthened defenses against model and pipeline level vulnerabilities such as prompt injection, data poisoning, and tool misuse, these system centric approaches may fail
SeCon-RAG: A Two-Stage Semantic Filtering and Conflict-Free Framework for Trustworthy RAG
Retrieval-augmented generation (RAG) systems enhance large language models (LLMs) with external knowledge but are vulnerable to corpus poisoning and contamination attacks, which can compromise output integrity. Existing defenses often
Robust Backdoor Removal by Reconstructing Trigger-Activated Changes in Latent Representation
Backdoor attacks pose a critical threat to machine learning models, causing them to behave normally on clean data but misclassify poisoned data into a poisoned class. Existing defenses often attempt
Context-Aware Prompt Injection Detection via Small Language Models in RAG Pipelines: nokast-secureRAG
recall falls to near zero, indicating that fluent fact-poisoning attacks lie outside the detector's instruction-consistency threat model. We report this limitation alongside the in-distribution results
Poisoned Playbooks: Demystifying Knowledge Poisoning Effects on AI Security Agents
poisons about real-world 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
Reflections on Trusting Trust, Revisited: Contaminating Self-Modifying AI Coding Agents with Poisoned Benchmarks
experiments, we distill properties of the vulnerability, benchmark, model, and agent scaffolding that are sufficient to enable a benchmark poisoning attack. Moreover, we show that contamination often persists even when
Weight space Detection of Backdoors in LoRA Adapters
trigger for backdoor behavior is unknown. We detect poisoned adapters by analyzing their weight matrices directly, without running the model -- making our method data-agnostic. Our method extracts simple statistics
Trigger the Straggler: Load Hijack on Mixture-of-Experts LLMs
schedule. We introduce Load Hijack, in which a malicious model provider modifies only a checkpoint's router weights, distributes the poisoned checkpoint, and retains a private trigger. When the trigger
Authorization Before Context: A Model-Neutral Audience Boundary Against Cross-Audience Memory Leakage in Agentic Systems
narrower audience reaches a broader one and that poisoned memory cannot widen its own audience. The boundary is a model-neutral invariant on the exact assembled context: a forbidden fact
Securing Code Understanding: Detecting Natural Backdoor Vulnerability in Code Language Models
natural backdoors, in normally trained deep learning models. Despite posing threats as serious as those introduced through data poisoning, security implications of natural backdoor vulnerabilities in CodeLMs remain poorly understood
Secret Stealing Attacks on Local LLM Fine-Tuning through Supply-Chain Model Code Backdoors
viewed as a privacy boundary, we reveal that compromised model code is sufficient to steal them. Current passive pretrained-weight poisoning attacks, while effective for natural language, fundamentally fail