9 citations · 17 across the 11 of their papers we have counts for
12 papers · 1 filter
Not All Refusals Are Equal: How Safety Alignment Fails Cybersecurity at Scale
Vadym Hadetskyi, Dario Pasquini, Artem Sorokin
There is no doubt that safety alignment is an essential step in LLM training. However, conceptually it does not distinguish between various domains and the level of potential harm…
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…
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…
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…
Neural Exec: Learning (and Learning from) Execution Triggers for Prompt Injection Attacks
Dario Pasquini, Martin Strohmeier, Carmela Troncoso
We introduce a new family of prompt injection attacks, termed Neural Exec. Unlike known attacks that rely on handcrafted strings (e.g., "Ignore previous instructions and..."), we s…
Your Email Address Holds the Key: Understanding the Connection Between Email and Password Security with Deep Learning
Etienne Salimbeni, Nina Mainusch, Dario Pasquini
In this work, we investigate the effectiveness of deep-learning-based password guessing models for targeted attacks on human-chosen passwords. In recent years, service providers ha…
Universal Neural-Cracking-Machines: Self-Configurable Password Models from Auxiliary Data
Dario Pasquini, Giuseppe Ateniese, Carmela Troncoso
We introduce the concept of "universal password model" -- a password model that, once pre-trained, can automatically adapt its guessing strategy based on the target system. To achi…
On the (In)security of Peer-to-Peer Decentralized Machine Learning
Dario Pasquini, Mathilde Raynal, Carmela Troncoso
In this work, we carry out the first, in-depth, privacy analysis of Decentralized Learning -- a collaborative machine learning framework aimed at addressing the main limitations of…
Unleashing the Tiger: Inference Attacks on Split Learning
Dario Pasquini, Giuseppe Ateniese, Massimo Bernaschi
We investigate the security of Split Learning -- a novel collaborative machine learning framework that enables peak performance by requiring minimal resources consumption. In the p…
Reducing Bias in Modeling Real-world Password Strength via Deep Learning and Dynamic Dictionaries
Dario Pasquini, Marco Cianfriglia, Giuseppe Ateniese +1
Password security hinges on an in-depth understanding of the techniques adopted by attackers. Unfortunately, real-world adversaries resort to pragmatic guessing strategies such as…
Interpretable Probabilistic Password Strength Meters via Deep Learning
Dario Pasquini, Giuseppe Ateniese, Massimo Bernaschi
Probabilistic password strength meters have been proved to be the most accurate tools to measure password strength. Unfortunately, by construction, they are limited to solely produ…
Improving Password Guessing via Representation Learning
Dario Pasquini, Ankit Gangwal, Giuseppe Ateniese +2
Learning useful representations from unstructured data is one of the core challenges, as well as a driving force, of modern data-driven approaches. Deep learning has demonstrated t…