AI Threat Alert
Survey MEDIUM
Position: Privacy Is Not Just Memorization!
Niloofar Mireshghallah, Tianshi Li
The discourse on privacy risks in Large Language Models (LLMs) has disproportionately focused on verbatim memorization of training data, while a...
AI Security Research
2,529+ academic papers on AI security, attacks, and defenses
Total
2,529
Attack
969
Benchmark
729
Defense
345
Tool
272
Survey
142
Showing 1141–1160 of 1,207 papers
Clear filters Attack MEDIUM
Securing generative artificial intelligence with parallel magnetic tunnel junction true randomness
Youwei Bao, Shuhan Yang, Hyunsoo Yang
Deterministic pseudo random number generators (PRNGs) used in generative artificial intelligence (GAI) models produce predictable patterns vulnerable...
Attack MEDIUM
AdvEvo-MARL: Shaping Internalized Safety through Adversarial Co-Evolution in Multi-Agent Reinforcement Learning
Zhenyu Pan, Yiting Zhang, Zhuo Liu +13 more
LLM-based multi-agent systems excel at planning, tool use, and role coordination, but their openness and interaction complexity also expose them to...
Benchmark MEDIUM
POLAR: Automating Cyber Threat Prioritization through LLM-Powered Assessment
Luoxi Tang, Yuqiao Meng, Ankita Patra +3 more
Large Language Models (LLMs) are intensively used to assist security analysts in counteracting the rapid exploitation of cyber threats, wherein LLMs...
Attack MEDIUM
Bypassing Prompt Guards in Production with Controlled-Release Prompting
Jaiden Fairoze, Sanjam Garg, Keewoo Lee +1 more
As large language models (LLMs) advance, ensuring AI safety and alignment is paramount. One popular approach is prompt guards, lightweight mechanisms...
Benchmark MEDIUM
OntoLogX: Ontology-Guided Knowledge Graph Extraction from Cybersecurity Logs with Large Language Models
Luca Cotti, Idilio Drago, Anisa Rula +2 more
System logs represent a valuable source of Cyber Threat Intelligence (CTI), capturing attacker behaviors, exploited vulnerabilities, and traces of...
Defense MEDIUM
Safety Instincts: LLMs Learn to Trust Their Internal Compass for Self-Defense
Guobin Shen, Dongcheng Zhao, Haibo Tong +3 more
Ensuring Large Language Model (LLM) safety remains challenging due to the absence of universal standards and reliable content validators, making it...
Benchmark MEDIUM
Downgrade to Upgrade: Optimizer Simplification Enhances Robustness in LLM Unlearning
Yicheng Lang, Yihua Zhang, Chongyu Fan +3 more
Large language model (LLM) unlearning aims to surgically remove the influence of undesired data or knowledge from an existing model while preserving...
Attack MEDIUM
Eyes-on-Me: Scalable RAG Poisoning through Transferable Attention-Steering Attractors
Yen-Shan Chen, Sian-Yao Huang, Cheng-Lin Yang +1 more
Existing data poisoning attacks on retrieval-augmented generation (RAG) systems scale poorly because they require costly optimization of poisoned...
Benchmark MEDIUM
Sentry: Authenticating Machine Learning Artifacts on the Fly
Andrew Gan, Zahra Ghodsi
Machine learning systems increasingly rely on open-source artifacts such as datasets and models that are created or hosted by other parties. The...
Tool MEDIUM
PolyLink: A Blockchain Based Decentralized Edge AI Platform for LLM Inference
Hongbo Liu, Jiannong Cao, Bo Yang +7 more
The rapid advancement of large language models (LLMs) in recent years has revolutionized the AI landscape. However, the deployment model and usage of...
Attack MEDIUM
Understanding Sensitivity of Differential Attention through the Lens of Adversarial Robustness
Tsubasa Takahashi, Shojiro Yamabe, Futa Waseda +1 more
Differential Attention (DA) has been proposed as a refinement to standard attention, suppressing redundant or noisy context through a subtractive...
Attack MEDIUM
Has the Two-Decade-Old Prophecy Come True? Artificial Bad Intelligence Triggered by Merely a Single-Bit Flip in Large Language Models
Yu Yan, Siqi Lu, Yang Gao +4 more
Recently, Bit-Flip Attack (BFA) has garnered widespread attention for its ability to compromise software system integrity remotely through hardware...
Tool MEDIUM
Cloud Investigation Automation Framework (CIAF): An AI-Driven Approach to Cloud Forensics
Dalal Alharthi, Ivan Roberto Kawaminami Garcia
Large Language Models (LLMs) have gained prominence in domains including cloud security and forensics. Yet cloud forensic investigations still rely...
Attack MEDIUM
A Call to Action for a Secure-by-Design Generative AI Paradigm
Dalal Alharthi, Ivan Roberto Kawaminami Garcia
Large language models have gained widespread prominence, yet their vulnerability to prompt injection and other adversarial attacks remains a critical...
Attack MEDIUM
MOLM: Mixture of LoRA Markers
Samar Fares, Nurbek Tastan, Noor Hussein +1 more
Generative models can generate photorealistic images at scale. This raises urgent concerns about the ability to detect synthetically generated images...
Benchmark MEDIUM
SecureBERT 2.0: Advanced Language Model for Cybersecurity Intelligence
Ehsan Aghaei, Sarthak Jain, Prashanth Arun +1 more
Effective analysis of cybersecurity and threat intelligence data demands language models that can interpret specialized terminology, complex document...
Attack MEDIUM
CHAI: Command Hijacking against embodied AI
Luis Burbano, Diego Ortiz, Qi Sun +5 more
Embodied Artificial Intelligence (AI) promises to handle edge cases in robotic vehicle systems where data is scarce by using common-sense reasoning...
Attack MEDIUM
Are Robust LLM Fingerprints Adversarially Robust?
Anshul Nasery, Edoardo Contente, Alkin Kaz +2 more
Model fingerprinting has emerged as a promising paradigm for claiming model ownership. However, robustness evaluations of these schemes have mostly...
Benchmark MEDIUM
Fairness Testing in Retrieval-Augmented Generation: How Small Perturbations Reveal Bias in Small Language Models
Matheus Vinicius da Silva de Oliveira, Jonathan de Andrade Silva, Awdren de Lima Fontao
Large Language Models (LLMs) are widely used across multiple domains but continue to raise concerns regarding security and fairness. Beyond known...
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