AI Threat AlertMembrane: A Self-Evolving Contrastive Safety Memory for LLM Agent Defense
mechanism. At inference, retrieved cells serve as grounding context for precise safety decisions. Across model-level safety on HarmBench and agent-level safety on AgentHarm, Membrane achieves the highest
Fight Poison with Poison: Enhancing Robustness in Few-shot Machine-Generated Text Detection with Adversarial Training
Machine-generated text (MGT) detection is critical for regulating online
Beyond Small Patches: Black-Box Detection and Purification of Diverse Backdoor Triggers
defenses often rely on model internals, training data, or clean validation samples, making them difficult to deploy when only black-box access to a trained model is available. We propose
CIS-BA: Continuous Interaction Space Based Backdoor Attack for Object Detection in the Real-World
Object detection models deployed in real-world applications such as autonomous driving face serious threats from backdoor attacks. Despite their practical effectiveness,existing methods are inherently limited in both capability
Overcoming the Retrieval Barrier: Indirect Prompt Injection in the Wild for LLM Systems
user query on OpenAI's embedding models), and achieves near-100% retrieval across 11 benchmarks and 8 embedding models (including both open-source models and proprietary services). Based on this
Privacy Preserving Machine Learning Workflow: from Anonymization to Personalized Differential Privacy Budgets in Federated Learning
called privacy preserving machine learning architectures, such as federated learning. While federated learning enables model training on decentralized data preventing their sharing and centralization, it still faces several challenges related
SoK: Agentic Retrieval-Augmented Generation (RAG): Taxonomy, Architectures, Evaluation, and Research Directions
Retrieval-Augmented Generation (RAG) systems are increasingly evolving into agentic architectures where large language models autonomously coordinate multi-step reasoning, dynamic memory management, and iterative retrieval strategies. Despite rapid industrial
Adaptive Intrusion Detection System using Transformer-Based Neural Networks and Continual Learning Approach with Adversarial Investigation
fixed traffic snapshots decay silently after deployment as threat distributions shift. Fine-tuning models on new attacks triggers catastrophic forgetting, while retraining from scratch is computationally infeasible. Replay-based continual
The Containment Gap: How Deployed Agentic AI Frameworks Fail Public-Facing Safety Requirements
Agentic large language model systems that autonomously invoke tools, maintain persistent memory, and execute multi-step plans are increasingly deployed in public-facing domains, including government services, healthcare triage
STELP: Secure Transpilation and Execution of LLM-Generated Programs
Rapid evolution of Large Language Models (LLMs) has achieved major advances in reasoning, planning, and function-calling capabilities. Multi-agentic collaborative frameworks using such LLMs place them at the center
BackdoorVLM: A Benchmark for Backdoor Attacks on Vision-Language Models
hijack. Each category captures a distinct pathway through which an adversary can manipulate a model's behavior. We evaluate these threats using 12 representative attack methods spanning text, image
SEAL: Towards Persistent Tool Poisoning Attack against LLM Agents via Prefix-Eliciting Trigger
rounds. To investigate whether tool poisoning remains a practical threat in such realistic settings, we propose SEAL, a two-stage method for persistent tool poisoning. Specifically, SEAL first performs Prefix
Secure Semantic Communications via AI Defenses: Fundamentals, Solutions, and Future Directions
SemCom via AI defense. We analyze AI-centric threat models by consolidating existing studies and organizing attack surfaces across model-level, channel-realizable, knowledge-based, and networked inference vectors. Building
ReShift: Aha-Moment-Driven Reasoning-Level Backdoor Attacks on Vision-Language Models
Vision--Language Models (VLMs) are increasingly deployed in safety-critical applications, yet remain vulnerable to backdoor attacks. Existing methods primarily manipulate final outputs, often producing reasoning traces that are inconsistent
Contagion on the Trading Floor: How Adversarial Signals Spread in Multi-Agent Trading Systems
Multi-agent trading systems built on large language models (LLMs) are beginning to appear in quantitative finance, yet their robustness to adversarial inputs is largely unknown. We study the vulnerability
Safer Content or Firmer Refusals? A Hybrid Perturbation Defense for Alignment under Harmful Fine-tuning
fine-tuning set can degrade the model's alignment. Two recent alignment-stage defenses address this problem at different levels of the model. Vaccine improves the robustness of hidden embeddings
Bypassing Prompt Injection Detectors through Evasive Injections
Large language models (LLMs) are increasingly used in interactive and retrieval-augmented systems, but they remain vulnerable to task drift; deviations from a user's intended instruction due to injected
Autonomous Cyber Defense in Connected Vehicles: A Multi-Agent Approach to V2X Security
resolving safety-security conflicts using complementary sensor observations. The cloud tier refines detection models through Byzantine fault-tolerant federated learning and redistributes updated weights to the fleet. Every timing constraint
Exploring the Security Threats of Retriever Backdoors in Retrieval-Augmented Code Generation
Retrieval-Augmented Code Generation (RACG) is increasingly adopted to enhance Large Language Models for software development, yet its security implications remain dangerously underexplored. This paper conducts the first systematic exploration
How to Trick Your AI TA: A Systematic Study of Academic Jailbreaking in LLM Code Evaluation
Large Language Models (LLMs) as automatic judges for code evaluation is becoming increasingly prevalent in academic environments. But their reliability can be compromised by students who may employ adversarial prompting