2,529+ academic papers on AI security, attacks, and defenses
Showing 1–7 of 7 papers
Clear filtersBen Kereopa-Yorke, Guillermo Diaz, Holly Wright +3 more
We define Oracle Poisoning, an attack class in which an adversary corrupts a structured knowledge graph that AI agents query at runtime via tool-use...
Li Lixing
Modern large language models (LLMs) rely on system prompts to establish behavioral constraints and safety rules. Standard causal self-attention...
Isaac David, Arthur Gervais
Security updates create a short but important window in which defenders and attackers can compare vulnerable and patched software. Yet in many...
Samuel Korn
Retrieval-Augmented Generation (RAG) systems are vulnerable to knowledge base poisoning, yet existing attacks have been evaluated almost exclusively...
Yiwei Zhang, Jeremiah Birrell, Reza Ebrahimi +3 more
Large language models (LLMs) remain vulnerable to adversarial prompting despite advances in alignment and safety, often exhibiting harmful behaviors...
Marco Rando, Samuel Vaiter
Large language models (LLMs) are known to be vulnerable to jailbreak attacks, which typically rely on carefully designed prompts containing explicit...
Jan Dolejš, Martin Jureček, Róbert Lórencz
Modern malware detection pipelines rely on continuous data ingestion and machine learning to counter the high volume of novel threats. This work...
Get breaking CVE alerts, compliance reports (ISO 42001, EU AI Act), and CISO risk assessments for your AI/ML stack.
Start 14-Day Free Trial
AI Threat Alert