AI Threat Alert
Benchmark HIGH
Red Teaming Large Reasoning Models
Jiawei Chen, Yang Yang, Chao Yu +6 more
Large Reasoning Models (LRMs) have emerged as a powerful advancement in multi-step reasoning tasks, offering enhanced transparency and logical...
AI Security Research
2,583+ academic papers on AI security, attacks, and defenses
Total
2,583
Attack
994
Benchmark
740
Defense
355
Tool
275
Survey
146
Showing 1801–1820 of 1,934 papers
Clear filters Attack HIGH
WARP: Weight Teleportation for Attack-Resilient Unlearning Protocols
Mohammad M Maheri, Xavier Cadet, Peter Chin +1 more
Approximate machine unlearning aims to efficiently remove the influence of specific data points from a trained model, offering a practical...
Defense MEDIUM
SD-CGAN: Conditional Sinkhorn Divergence GAN for DDoS Anomaly Detection in IoT Networks
Henry Onyeka, Emmanuel Samson, Liang Hong +3 more
The increasing complexity of IoT edge networks presents significant challenges for anomaly detection, particularly in identifying sophisticated...
Benchmark MEDIUM
Evaluating LLMs for One-Shot Patching of Real and Artificial Vulnerabilities
Aayush Garg, Zanis Ali Khan, Renzo Degiovanni +1 more
Automated vulnerability patching is crucial for software security, and recent advancements in Large Language Models (LLMs) present promising...
Defense MEDIUM
Are LLMs Good Safety Agents or a Propaganda Engine?
Neemesh Yadav, Francesco Ortu, Jiarui Liu +5 more
Large Language Models (LLMs) are trained to refuse to respond to harmful content. However, systematic analyses of whether this behavior is truly a...
Attack MEDIUM
An Empirical Study on the Security Vulnerabilities of GPTs
Tong Wu, Weibin Wu, Zibin Zheng
Equipped with various tools and knowledge, GPTs, one kind of customized AI agents based on OpenAI's large language models, have illustrated great...
Defense HIGH
Retrieval-Augmented Few-Shot Prompting Versus Fine-Tuning for Code Vulnerability Detection
Fouad Trad, Ali Chehab
Few-shot prompting has emerged as a practical alternative to fine-tuning for leveraging the capabilities of large language models (LLMs) in...
Benchmark LOW
TIM-PRM: Verifying multimodal reasoning with Tool-Integrated PRM
Peng Kuang, Xiangxiang Wang, Wentao Liu +2 more
Multimodal Large Language Models (MLLMs) have achieved impressive performances in mathematical reasoning, yet they remain vulnerable to visual...
Tool MEDIUM
MAS-Shield: A Defense Framework for Secure and Efficient LLM MAS
Kaixiang Wang, Zhaojiacheng Zhou, Bunyod Suvonov +2 more
Large Language Model (LLM)-based Multi-Agent Systems (MAS) are susceptible to linguistic attacks that can trigger cascading failures across the...
Benchmark MEDIUM
Watermarks for Embeddings-as-a-Service Large Language Models
Anudeex Shetty
Large Language Models (LLMs) have demonstrated exceptional capabilities in natural language understanding and generation. Based on these LLMs,...
Attack MEDIUM
NetDeTox: Adversarial and Efficient Evasion of Hardware-Security GNNs via RL-LLM Orchestration
Zeng Wang, Minghao Shao, Akashdeep Saha +4 more
Graph neural networks (GNNs) have shown promise in hardware security by learning structural motifs from netlist graphs. However, this reliance on...
Benchmark MEDIUM
Ghosting Your LLM: Without The Knowledge of Your Gradient and Data
Abeer Matar A. Almalky, Ziyan Wang, Mohaiminul Al Nahian +2 more
In recent years, large language models (LLMs) have achieved substantial advancements and are increasingly integrated into critical applications...
Benchmark MEDIUM
CacheTrap: Injecting Trojans in LLMs without Leaving any Traces in Inputs or Weights
Mohaiminul Al Nahian, Abeer Matar A. Almalky, Gamana Aragonda +6 more
Adversarial weight perturbation has emerged as a concerning threat to LLMs that either use training privileges or system-level access to inject...
Survey LOW
AI Deception: Risks, Dynamics, and Controls
Boyuan Chen, Sitong Fang, Jiaming Ji +57 more
As intelligence increases, so does its shadow. AI deception, in which systems induce false beliefs to secure self-beneficial outcomes, has evolved...
Attack HIGH
Evaluating the Robustness of Large Language Model Safety Guardrails Against Adversarial Attacks
Richard J. Young
Large Language Model (LLM) safety guardrail models have emerged as a primary defense mechanism against harmful content generation, yet their...
Attack HIGH
Distillability of LLM Security Logic: Predicting Attack Success Rate of Outline Filling Attack via Ranking Regression
Tianyu Zhang, Zihang Xi, Jingyu Hua +1 more
In the realm of black-box jailbreak attacks on large language models (LLMs), the feasibility of constructing a narrow safety proxy, a lightweight...
Tool MEDIUM
A Safety and Security Framework for Real-World Agentic Systems
Shaona Ghosh, Barnaby Simkin, Kyriacos Shiarlis +9 more
This paper introduces a dynamic and actionable framework for securing agentic AI systems in enterprise deployment. We contend that safety and...
Benchmark MEDIUM
Beyond Membership: Limitations of Add/Remove Adjacency in Differential Privacy
Gauri Pradhan, Joonas Jälkö, Santiago Zanella-Bèguelin +1 more
Training machine learning models with differential privacy (DP) limits an adversary's ability to infer sensitive information about the training data....
Benchmark LOW
MADRA: Multi-Agent Debate for Risk-Aware Embodied Planning
Junjian Wang, Lidan Zhao, Xi Sheryl Zhang
Ensuring the safety of embodied AI agents during task planning is critical for real-world deployment, especially in household environments where...
Benchmark MEDIUM
The Phish, The Spam, and The Valid: Generating Feature-Rich Emails for Benchmarking LLMs
Rebeka Toth, Tamas Bisztray, Nils Gruschka
In this paper, we introduce a metadata-enriched generation framework (PhishFuzzer) that seeds real emails into Large Language Models (LLMs) to...
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