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 101–120 of 562 papers
Clear filtersGosha Kozhevnikov, Tyler Tracy
We present Screwtape, a pipeline that converts real agent transcripts into attacks that evade action-only monitors. An LLM attacker edits a...
Qianlong Lan, Anuj Kaul, Vinothini Pandurangan +1 more
Prompt-injection defenses are commonly evaluated as binary detectors, although deployed LLM applications must make operational decisions about...
Joas Antonio dos Santos Barbosa
LLM coding agents such as Claude Code and OpenAI Codex can drive penetration-testing tools, but they reason about an engagement as a flat list of...
Thuy Dung Nguyen, Jingyi Guo, Rana Seda Dubauskas +3 more
LLM-based agents increasingly execute long-horizon, end-to-end workflows involving shell commands, code modification, dependency installation, and...
Mohsen Iranmanesh, Sina Moradi Sabet, Sina Marefat +7 more
Large language models are increasingly deployed as autonomous agents for automated program repair, patching software vulnerabilities through...
Yixin Tan, Chia-Yi Hsu, Jiayang Liu +2 more
Automated jailbreak attacks rely on evaluation feedback to optimize adversarial prompts, but existing methods often lack fine-grained diagnostic...
Florian Spelter, Johannes F. Loevenich, Tobias Hürten +4 more
Although Artificial Intelligence (AI)-based Intrusion Detection Systems (IDSs) are increasingly deployed in operational networks, their dependence on...
Adel Khorramrouz, Yi-Chia Wang, Sharon Levy
Social psychology has long described how people can be persuaded to interpret a harmful act as moral. Recent research suggests that Large Language...
Sarthak Gupta, Priyanka Tembey
Several recent studies propose detecting prompt injections from a language model's internal state. However, these methods do not measure a single...
Liam Davis, Romain Emil-Popescu, Suman Jana
Jailbreak attacks against large language models (LLMs) are evaluated using attack success rate (ASR), which depends on correctly identifying...
Kshitij Mishra, Salem Lahlou, Nils Lukas
Indirect prompt injection uses untrusted retrieved content, tool outputs, or agent messages to override an LLM's intended behavior. We show that...
Bangya Liu, Jianhong Tu, Yueyang Pan +18 more
AI agents are demonstrating increasingly strong offensive and defensive cybersecurity capabilities in the real world. Current benchmarks mostly...
Xi Chen, Chunyi Zhou, Rui Zeng +3 more
Federated fine-tuning of large language models (LLMs) allows multiple clients to collaboratively adapt models without sharing raw data, offering...
Kaan Kale, Sriram Vishwanath
Linear probes on a model's hidden states are used as cheap monitors for indirect prompt injection: they screen the tool outputs an agent reads and...
Yan-Lun Chen, Pin-Yu Chen, Chia-Mu Yu
Third-party skills can expose LLM agents to malicious instructions hidden by encoding. We introduce Ciphered Skill Injection (CSI), which embeds such...
Fasheng Miao, Zhaoyu Hu, Xiang Li +5 more
Large language model (LLM) agents are increasingly used for vulnerability analysis, yet existing benchmarks provide limited coverage of exploitation...
Xiang Li, Aichun Huang, Mingming Zhang +1 more
TypeSafe AI's Jev is a new kind of safety judge. It generates no text and no label: it answers questions the developer defines in advance and returns...
Maria-Ioana-Andrea Ivan, Orhan Ermis, Ruxandra F. Olimid
With recent developments in Artificial Intelligence (AI), the adoption of distributed learning paradigms such as Federated Learning (FL) has emerged....
Yuetian Chen, Kaiyuan Zhang, Yuntao Du +6 more
Browser agents need to make decisions and execute actions on behalf of users while consuming untrusted webpages, creating opportunities for malicious...
Ali Javeri, Javad Hezareh, Radin Shahdaei +1 more
Score-based black-box adversarial attacks pose a serious threat to deployed machine learning systems by exploiting repeated model queries to craft...
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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