AI Threat Alert
Serverless AI Security: Attack Surface Analysis and Runtime Protection Mechanisms for FaaS-Based Machine Learning
characterize the attack surface across five categories: function-level vulnerabilities (cold start exploitation, dependency poisoning), model-specific threats (API-based extraction, adversarial inputs), infrastructure attacks (cross-function contamination, privilege escalation
Web Technologies Security in the AI Era: A Survey of CDN-Enhanced Defenses
mitigate while reducing data movement and enhancing compliance, yet introduces new risks around model abuse, poisoning, and governance
Threat Modeling and Attack Surface Analysis of IoT-Enabled Controlled Environment Agriculture Systems
federated transfer learning, adversarial agronomic schedules that exploit crop biology rather than computational models, and reward poisoning of reinforcement-learning energy optimizers. Physical impact analysis quantifies crop loss timelines from
MirageBackdoor: A Stealthy Attack that Induces Think-Well-Answer-Wrong Reasoning
MirageBD generally achieves over 90% attack success rate across four datasets and five models with a poison ratio of only 5%. Moreover, even under rigorous evaluations such as trigger perturbations
Secure Retrieval-Augmented Generation against Poisoning Attacks
Large language models (LLMs) have transformed natural language processing (NLP), enabling applications from content generation to decision support. Retrieval-Augmented Generation (RAG) improves LLMs by incorporating external knowledge but also
Fully Homomorphic Encryption on Llama 3 model for privacy preserving LLM inference
insecure LLM pipelines, making them vulnerable to multiple attacks such as data poisoning, prompt injection, and model theft. Although several security techniques (input/output sanitization, decentralized learning, access control management
Trojan Hippo: Weaponizing Agent Memory for Data Exfiltration
class of persistent memory attacks that operates in a more realistic threat model than prior memory poisoning work: the attacker plants a dormant payload into an agent's long-term
Stealthy and Adjustable Text-Guided Backdoor Attacks on Multimodal Pretrained Models
significantly improving stealthiness and practicality. Furthermore, we introduce visual adversarial perturbations on poisoned samples to modulate the model's learning of textual triggers, enabling a controllable and adjustable TGB attack
A Wolf in Sheep's Clothing: Targeted Routing Hijacking in Federated RAG
including missing evidence, poisoning, incorrect answers, and hallucinations. In a high-stakes MedQA-USMLE case study, we further show that poisoned retrieved evidence can mislead models across scales, leading
MemoryGraft: Persistent Compromise of LLM Agents via Poisoned Experience Retrieval
Large Language Model (LLM) agents increasingly rely on long-term memory and Retrieval-Augmented Generation (RAG) to persist experiences and refine future performance. While this experience learning capability enhances agentic
FedFG: Privacy-Preserving and Robust Federated Learning via Flow-Matching Generation
data; on the other hand, they may compromise clients to launch poisoning attacks that corrupt the global model. To balance accuracy and security, we propose FedFG, a robust FL framework
FLARE: Adaptive Multi-Dimensional Reputation for Robust Client Reliability in Federated Learning
learning (FL) enables collaborative model training while preserving data privacy. However, it remains vulnerable to malicious clients who compromise model integrity through Byzantine attacks, data poisoning, or adaptive adversarial behaviors
Repurposing and Evaluating the (In)Feasibility of Dataset Poisoning enabled Watermarking for Contrastive Learning
third-party or internet data is common. Recent studies show CL models are vulnerable to data-poisoning backdoor attacks, but their generalization and robustness are underexplored. We systematically evaluate existing
From Theory to Practice: Evaluating Data Poisoning Attacks and Defenses in In-Context Learning on Social Media Health Discourse
This study explored how in-context learning (ICL) in large language models can be disrupted by data poisoning attacks in the setting of public health sentiment analysis. Using tweets
Cisco Integrated AI Security and Safety Framework Report
threats now span content safety failures (e.g., harmful or deceptive outputs), model and data integrity compromise (e.g., poisoning, supply-chain tampering), runtime manipulations (e.g., prompt injection, tool and agent misuse
SCOUT: A Defense Against Data Poisoning Attacks in Fine-Tuned Language Models
Backdoor attacks create significant security threats to language models by
Stealthy Poisoning Attacks Bypass Defenses in Regression Settings
natural and physical sciences, yet their robustness to poisoning has received less attention. When it has, studies often assume unrealistic threat models and are thus less useful in practice
State Backdoor: Towards Stealthy Real-world Poisoning Attack on Vision-Language-Action Model in State Space
Vision-Language-Action (VLA) models are widely deployed in safety
Osmosis Distillation: Model Hijacking with the Fewest Samples
generated by dataset distillation methods, where an adversary can perform a model hijacking attack with only a few poisoned samples in the synthetic dataset. To reveal this threat, we propose
Needle-in-RAG: Prompt-Conditioned Character-Level Traceback of Poisoned Spans in Retrieved Evidence
evidence, but it also opens a data-layer attack surface: poisoned corpus entries can steer outputs without changing model parameters. Existing defenses and traceback methods are largely passage-level, which