AI Threat Alert indexes 4,319+ 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 541–560 of 1,229 papers
Clear filtersPablo Mateo-Torrejón, Alfonso Sánchez-Macián
The rapid integration of Large Language Models (LLMs) into Multi-Agent Systems (MAS) has significantly enhanced their collaborative problem-solving...
Zijun Feng, Yuming Feng, Yu Wang +4 more
Cross-chain bridges, the critical infrastructure of the multi-chain ecosystem, have become a primary target for attackers, resulting in over $2.8...
Víctor Mayoral-Vilches, María Sanz-Gómez, Francesco Balassone +6 more
As LLM-driven agents advance in cybersecurity, Jeopardy CTF benchmarks are approaching saturation and cyber ranges, the natural next evaluation...
Eungyu Woo, Yooshin Kim, Wonje Heo +1 more
Industrial Control Systems (ICS) integrate computing, physical processes, and communication to operate critical infrastructures such as power grids,...
Priyal Deep, Shane Emmons, Amy Fox +3 more
LLM-powered applications routinely embed secrets in system prompts, yet models can be tricked into revealing them. We built an adaptive attacker that...
Qi Li, Bo Yin, Weiqi Huang +6 more
Vision-Language-Action (VLA) models are emerging as a unified substrate for embodied intelligence. This shift raises a new class of safety...
Pegah Khayatan, Jayneel Parekh, Arnaud Dapogny +3 more
Despite impressive progress in capabilities of large vision-language models (LVLMs), these systems remain vulnerable to hallucinations, i.e., outputs...
Yuchen Shi, Xin Guo, Huajie Chen +3 more
Poisoning-based backdoor attacks pose significant threats to deep neural networks by embedding triggers in training data, causing models to...
Vishal Rajput
We prove that empirical risk minimisation (ERM) imposes a necessary geometric constraint on learned representations: any encoder that minimises...
Yongcan Yu, Lingxiao He, Jian Liang +5 more
Test-time reinforcement learning (TTRL) always adapts models at inference time via pseudo-labeling, leaving it vulnerable to spurious optimization...
Ari Azarafrooz
AI-agent guardrails are memoryless: each message is judged in isolation, so an adversary who spreads a single attack across dozens of sessions slips...
Mohammad Farhad, Shuvalaxmi Dass
Software security relies on effective vulnerability detection and patching, yet determining whether a patch fully eliminates risk remains an...
Hanzhi Liu, Chaofan Shou, Xiaonan Liu +4 more
LLM agents have begun to find real security vulnerabilities that human auditors and automated fuzzers missed for decades, in source-available targets...
Hoang Nguyen, Lu Wang, Marta Gaia Bras
Freight brokerages negotiate thousands of carrier rates daily under dynamic pricing conditions where models frequently revise targets...
He Yang Yuan, Xin Wang, Kundi Yao +3 more
Logging code plays an important role in software systems by recording key events and behaviors, which are essential for debugging and monitoring....
Girish, Mohd Mujtaba Akhtar, Orchid Chetia Phukan +1 more
The rapid advancement of Audio Large Language Models (ALMs), driven by Neural Audio Codecs (NACs), has led to the emergence of highly realistic...
Robert Stanley, Avi Verma, Lillian Tsai +2 more
AI agents promise to serve as general-purpose personal assistants for their users, which requires them to have access to private user data (e.g.,...
Alankrit Chona, Igor Kozlov, Ambuj Kumar
We introduce the Cyber Defense Benchmark, a benchmark for measuring how well large language model (LLM) agents perform the core SOC analyst task of...
Alankrit Chona, Igor Kozlov, Ambuj Kumar
We introduce the Cyber Defense Benchmark, a benchmark for measuring how well large language model (LLM) agents perform the core SOC analyst task of...
Ali Al-Kaswan, Maksim Plotnikov, Maxim Hájek +3 more
Large Language Model (LLM) agents are increasingly proposed for autonomous cybersecurity tasks, but their capabilities in realistic offensive...
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 4,319+ 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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