AI Threat Alert indexes 3,082+ peer-reviewed and preprint papers on AI/ML security — covering adversarial attacks, model defenses, red-teaming benchmarks, surveys, and security tooling. Papers are sourced from arXiv, classified by type and by relevance to real-world threats, and cross-referenced with the CVEs and incidents they relate to.
Showing 1441–1460 of 3,082 papers
Anudeep Das, Prach Chantasantitam, Gurjot Singh +3 more
Large language models (LLMs) are increasingly deployed in settings where inducing a bias toward a certain topic can have significant consequences,...
Xu Li, Simon Yu, Minzhou Pan +5 more
LLM-based agents are becoming increasingly capable, yet their safety lags behind. This creates a gap between what agents can do and should do. This...
Yiran Gao, Kim Hammar, Tao Li
Rapidly evolving cyberattacks demand incident response systems that can autonomously learn and adapt to changing threats. Prior work has extensively...
Alfous Tim, Kuniyilh Simi D
The Internet of Things (IoT) systems increasingly depend on continual learning to adapt to non-stationary environments. These environments can...
George Alexandru Adam, Alexander Cui, Edwin Thomas +7 more
While historical considerations surrounding text authenticity revolved primarily around plagiarism, the advent of large language models (LLMs) has...
Osama Zafar, Shaojie Zhan, Tianxi Ji +1 more
In recent years, the widespread adoption of Machine Learning as a Service (MLaaS), particularly in sensitive environments, has raised considerable...
Tailia Malloy, Tegawende F. Bissyande
Large Language Models are expanding beyond being a tool humans use and into independent agents that can observe an environment, reason about...
Nataša Krčo, Zexi Yao, Matthieu Meeus +1 more
Data containing personal information is increasingly used to train, fine-tune, or query Large Language Models (LLMs). Text is typically scrubbed of...
Jiyong Uhm, Minseok Kim, Michalis Polychronakis +1 more
Binary code analysis plays an essential role in cybersecurity, facilitating reverse engineering to reveal the inner workings of programs in the...
Oguzhan Baser, Elahe Sadeghi, Eric Wang +5 more
Most large language models (LLMs) run on external clouds: users send a prompt, pay for inference, and must trust that the remote GPU executes the LLM...
Rosie Zhao, Anshul Shah, Xiaoyu Zhu +5 more
Reinforcement learning (RL) fine-tuning has become a key technique for enhancing large language models (LLMs) on reasoning-intensive tasks,...
André Storhaug, Jiamou Sun, Jingyue Li
Identifying vulnerability-fixing commits corresponding to disclosed CVEs is essential for secure software maintenance but remains challenging at...
Renjun Xu, Yang Yan
The transition from monolithic language models to modular, skill-equipped agents marks a defining shift in how large language models (LLMs) are...
Yannick Assogba, Jacopo Cortellazzi, Javier Abad +3 more
Jailbreak attacks remain a persistent threat to large language model safety. We propose Context-Conditioned Delta Steering (CC-Delta), an SAE-based...
Nate Rahn, Allison Qi, Avery Griffin +3 more
We want language model assistants to conform to a character specification, which asserts how the model should act across diverse user interactions....
Yuepeng Hu, Yuqi Jia, Mengyuan Li +2 more
In a malicious tool attack, an attacker uploads a malicious tool to a distribution platform; once a user installs the tool and the LLM agent selects...
Zhaoxin Wang, Jiaming Liang, Fengbin Zhu +5 more
Large language models (LLMs) and multimodal LLMs are typically safety-aligned before release to prevent harmful content generation. However, recent...
Yujun Zhou, Yue Huang, Han Bao +8 more
While most AI alignment research focuses on preventing models from generating explicitly harmful content, a more subtle risk is emerging:...
Varpu Vehomäki, Kimmo K. Kaski
Understanding cyber security is increasingly important for individuals and organizations. However, a lot of information related to cyber security can...
Christian Rondanini, Barbara Carminati, Elena Ferrari +2 more
The proliferation of edge devices has created an urgent need for security solutions capable of detecting malware in real time while operating under...
AI security research studies how AI and machine-learning systems can be attacked and defended — covering adversarial examples, prompt injection, model poisoning, training-data extraction, and the mitigations against them. AI Threat Alert curates this research from academic sources so security teams can track the threats behind emerging AI risks.
AI Threat Alert indexes 3,082+ papers on AI/ML security, classified across attack, defense, benchmark, survey, and tool categories and updated continuously.
Papers are sourced from arXiv, then classified by type and by relevance to real-world AI/ML threats, and cross-referenced with the CVEs and incidents they relate to.
Coverage spans adversarial attacks, model and system defenses, red-teaming benchmarks, literature surveys, and security tooling for LLMs, ML libraries, AI agents, and inference pipelines.
Every paper is filtered for AI security relevance and linked to the vulnerabilities, vendors, and incidents it relates to, so the research connects directly to operational threat intelligence.
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