2,529+ academic papers on AI security, attacks, and defenses
Showing 1–20 of 22 papers
Clear filtersMonika Jotautaitė, Maria Angelica Martinez, Ollie Matthews +1 more
We introduce a red-teaming methodology that exposes harder-to-catch attacks for coding-agent monitors, suggesting that current practices may...
Feiyue Xu, Hongsheng Hu, Chaoxiang He +9 more
Large Language Models (LLMs) have achieved remarkable success but remain highly susceptible to jailbreak attacks, in which adversarial prompts coerce...
Luyao Xu, Xiang Chen
Autonomous agent frameworks built upon large language models (LLMs) are evolving into complex, tool-integrated, and continuously operating systems,...
Yuming Xu, Mingtao Zhang, Zhuohan Ge +5 more
Retrieval-augmented generation (RAG) significantly enhances large language models (LLMs) but introduces novel security risks through external...
Charafeddine Mouzouni
LLM agents with tool access can discover and exploit security vulnerabilities. This is known. What is not known is which features of a system prompt...
Bhavuk Jain, Sercan Ö. Arık, Hardeo K. Thakur
Multimodal large language models (MLLMs) integrate information from multiple modalities such as text, images, audio, and video, enabling complex...
Shouqiao Wang, Marcello Politi, Samuele Marro +1 more
As agentic systems move into real-world deployments, their decisions increasingly depend on external inputs such as retrieved content, tool outputs,...
Dimitris Mitropoulos, Nikolaos Alexopoulos, Georgios Alexopoulos +1 more
Security code reviews increasingly rely on systems integrating Large Language Models (LLMs), ranging from interactive assistants to autonomous agents...
Fabrizio Dimino, Bhaskarjit Sarmah, Stefano Pasquali
The rapid adoption of large language models (LLMs) in financial services introduces new operational, regulatory, and security risks. Yet most...
Shruti Srivastava, Kiranmayee Janardhan, Shaurya Jauhari
Cybersecurity threats are becoming increasingly sophisticated, making traditional defense mechanisms and manual red teaming approaches insufficient...
Kunal Mukherjee
Trusted Execution Environments (TEEs) (e.g., Intel SGX and ArmTrustZone) aim to protect sensitive computation from a compromised operating system,...
Scott Thornton
AI-assisted code review is widely used to detect vulnerabilities before production release. Prior work shows that adversarial prompt manipulation can...
Peiran Wang, Xinfeng Li, Chong Xiang +5 more
The evolution of Large Language Models (LLMs) has resulted in a paradigm shift towards autonomous agents, necessitating robust security against...
George Tsigkourakos, Constantinos Patsakis
Static Application Security Testing (SAST) tools are integral to modern DevSecOps pipelines, yet tools like CodeQL, Semgrep, and SonarQube remain...
Luze Sun, Alina Oprea, Eric Wong
LLM-based vulnerability detectors are increasingly deployed in security-critical code review, yet their resilience to evasion under...
Pedro H. Barcha Correia, Ryan W. Achjian, Diego E. G. Caetano de Oliveira +5 more
The rapid advancement and widespread adoption of generative artificial intelligence (GenAI) and large language models (LLMs) has been accompanied by...
Masahiro Kaneko
The use of large language models (LLMs) in peer review systems has attracted growing attention, making it essential to examine their potential...
Panagiotis Theocharopoulos, Ajinkya Kulkarni, Mathew Magimai. -Doss
Large language models (LLMs) are increasingly considered for use in high-impact workflows, including academic peer review. However, LLMs are...
Devanshu Sahoo, Manish Prasad, Vasudev Majhi +5 more
Driven by surging submission volumes, scientific peer review has catalyzed two parallel trends: individual over-reliance on LLMs and institutional...
Strahinja Janjusevic, Anna Baron Garcia, Sohrob Kazerounian
Generative AI is reshaping offensive cybersecurity by enabling autonomous red team agents that can plan, execute, and adapt during penetration tests....
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