AI Threat AlertSalience Induction against Multi-Hop RAG Agents: Threat and Defense
Multi-Hop question answering, agents chain facts across documents. Existing defenses focus on content poisoning, which injects false facts, and prompt injection, which embeds directives. We identify a third attack
Fed-FBD: Federated Functional Block Diversification for Isolation, Privacy, and Surgical Unlearning
Federated learning (FL) enables collaborative model training without sharing raw
Provable Robustness against Backdoor Attacks via the Primal-Dual Perspective on Differential Privacy
Randomized smoothing is a powerful tool for certifying robustness to adversarial perturbations, including poisoning attacks via randomized training and evasion attacks via randomized inference. Extending these guarantees to backdoor attacks
Not What You Asked For: Typographic Attacks in Household Robot Manipulation
Open-vocabulary embodied AI agents increasingly rely on vision-language
When AI reviews science: Can we trust the referee?
The volume of scientific submissions continues to climb, outpacing the
SlowBA: An efficiency backdoor attack towards VLM-based GUI agents
Modern vision-language-model (VLM) based graphical user interface (GUI
SoK: Trust-Authorization Mismatch in LLM Agent Interactions
Large Language Models (LLMs) are evolving into autonomous agents capable of executing complex workflows via standardized protocols (e.g., MCP). However, this paradigm shifts control from deterministic code to probabilistic inference
SSCL-BW: Sample-Specific Clean-Label Backdoor Watermarking for Dataset Ownership Verification
dataset owners. Existing backdoor-based dataset ownership verification methods suffer from inherent limitations: poison-label watermarks are easily detectable due to label inconsistencies, while clean-label watermarks face high technical
Terrarium: Revisiting the Blackboard for Multi-Agent Safety, Privacy, and Security Studies
multi-agent system (MAS) powered by large language models (LLMs) can automate tedious user tasks such as meeting scheduling that requires inter-agent collaboration. LLMs enable nuanced protocols that account
Injection, Attack and Erasure: Revocable Backdoor Attacks via Machine Unlearning
networks (DNNs) due to their stealth and durability. While recent research has explored leveraging model unlearning mechanisms to enhance backdoor concealment, existing attack strategies still leave persistent traces that
"Your AI, My Shell": Demystifying Prompt Injection Attacks on Agentic AI Coding Editors
Agentic AI coding editors driven by large language models have recently become more popular due to their ability to improve developer productivity during software development. Modern editors such as Cursor