6 papers
Safe Language Generation in the Limit
Antonios Anastasopoulos, Giuseppe Ateniese, Evgenios M. Kornaropoulos
Recent results in learning a language in the limit have shown that, although language identification is impossible, language generation is tractable. As this foundational area expa…
The Coding Limits of Robust Watermarking for Generative Models
Danilo Francati, Yevin Nikhel Goonatilake, Shubham Pawar +2
We study a basic question about cryptographic watermarking for generative models: how reliable can a watermark remain when an adversary is allowed to corrupt the encoded signal? To…
When AIOps Become "AI Oops": Subverting LLM-driven IT Operations via Telemetry Manipulation
Dario Pasquini, Evgenios M. Kornaropoulos, Giuseppe Ateniese +3
AI for IT Operations (AIOps) is transforming how organizations manage complex software systems by automating anomaly detection, incident diagnosis, and remediation. Modern AIOps so…
Watermarks in the Sand: Impossibility of Strong Watermarking for Generative Models
Hanlin Zhang, Benjamin L. Edelman, Danilo Francati +3
Watermarking generative models consists of planting a statistical signal (watermark) in a model's output so that it can be later verified that the output was generated by the given…
LLMmap: Fingerprinting For Large Language Models
Dario Pasquini, Evgenios M. Kornaropoulos, Giuseppe Ateniese
We introduce LLMmap, a first-generation fingerprinting technique targeted at LLM-integrated applications. LLMmap employs an active fingerprinting approach, sending carefully crafte…
Hacking Back the AI-Hacker: Prompt Injection as a Defense Against LLM-driven Cyberattacks
Dario Pasquini, Evgenios M. Kornaropoulos, Giuseppe Ateniese
Large language models (LLMs) are increasingly being harnessed to automate cyberattacks, making sophisticated exploits more accessible and scalable. In response, we propose a new de…