1.5k citations · 2.3k across the 133 of their papers we have counts for
24 papers · 1 filter
SCyTAG: Scalable Cyber-Twin for Threat-Assessment Based on Attack Graphs
David Tayouri, Elad Duani, Abed Showgan +8
Understanding the risks associated with an enterprise environment is the first step toward improving its security. Organizations employ various methods to assess and prioritize the…
Training-Free Policy Violation Detection via Activation-Space Whitening in LLMs
Oren Rachmil, Avishag Shapira, Roy Betser +5
As organizations increasingly deploy LLMs in sensitive domains such as legal, financial, and medical settings, ensuring alignment with internal organizational policies has become a…
MIA-EPT: Membership Inference Attack via Error Prediction for Tabular Data
Eyal German, Daniel Samira, Yuval Elovici +1
Synthetic data generation plays an important role in enabling data sharing, particularly in sensitive domains like healthcare and finance. Recent advances in diffusion models have…
KubeGuard: LLM-Assisted Kubernetes Hardening via Configuration Files and Runtime Logs Analysis
Omri Sgan Cohen, Ehud Malul, Yair Meidan +3
The widespread adoption of Kubernetes (K8s) for orchestrating cloud-native applications has introduced significant security challenges, such as misconfigured resources and overly p…
SoK: Cybersecurity Assessment of Humanoid Ecosystem
Priyanka Prakash Surve, Asaf Shabtai, Yuval Elovici
Humanoids are progressing toward practical deployment across healthcare, industrial, defense, and service sectors. While typically considered cyber-physical systems (CPSs), their d…
FRAME : Comprehensive Risk Assessment Framework for Adversarial Machine Learning Threats
Avishag Shapira, Simon Shigol, Asaf Shabtai
The widespread adoption of machine learning (ML) systems increased attention to their security and emergence of adversarial machine learning (AML) techniques that exploit fundament…
LumiMAS: A Comprehensive Framework for Real-Time Monitoring and Enhanced Observability in Multi-Agent Systems
Ron Solomon, Yarin Yerushalmi Levi, Lior Vaknin +8
The incorporation of LLMs in multi-agent systems (MASs) has the potential to significantly improve our ability to autonomously solve complex problems. However, such systems introdu…
VAULT: Vigilant Adversarial Updates via LLM-Driven Retrieval-Augmented Generation for NLI
Roie Kazoom, Ofir Cohen, Rami Puzis +2
We introduce VAULT, a fully automated adversarial RAG pipeline that systematically uncovers and remedies weaknesses in NLI models through three stages: retrieval, adversarial gener…
Tab-MIA: A Benchmark Dataset for Membership Inference Attacks on Tabular Data in LLMs
Eyal German, Sagiv Antebi, Daniel Samira +2
Large language models (LLMs) are increasingly trained on tabular data, which, unlike unstructured text, often contains personally identifiable information (PII) in a highly structu…
ImpReSS: Implicit Recommender System for Support Conversations
Omri Haller, Yair Meidan, Dudu Mimran +2
Following recent advancements in large language models (LLMs), LLM-based chatbots have transformed customer support by automating interactions and providing consistent, scalable se…
ProfiLLM: An LLM-Based Framework for Implicit Profiling of Chatbot Users
Shahaf David, Yair Meidan, Ido Hersko +4
Despite significant advancements in conversational AI, large language model (LLM)-powered chatbots often struggle with personalizing their responses according to individual user ch…
ATAG: AI-Agent Application Threat Assessment with Attack Graphs
Parth Atulbhai Gandhi, Akansha Shukla, David Tayouri +4
Evaluating the security of multi-agent systems (MASs) powered by large language models (LLMs) is challenging, primarily because of the systems' complex internal dynamics and the ev…
LexiMark: Robust Watermarking via Lexical Substitutions to Enhance Membership Verification of an LLM's Textual Training Data
Eyal German, Sagiv Antebi, Edan Habler +2
Large language models (LLMs) can be trained or fine-tuned on data obtained without the owner's consent. Verifying whether a specific LLM was trained on particular data instances or…
FAA Framework: A Large Language Model-Based Approach for Credit Card Fraud Investigations
Shaun Shuster, Eyal Zloof, Asaf Shabtai +1
Credit card fraud mitigation plays a significant role in modern society. While fraud detection systems are essential, they often struggle to keep pace with the constantly evolving…
Mind the Web: The Security of Web Use Agents
Avishag Shapira, Parth Atulbhai Gandhi, Edan Habler +1
Web-use agents are rapidly being deployed to automate complex web tasks with extensive browser capabilities. However, these capabilities create a critical and previously unexplored…
RuleGenie: SIEM Detection Rule Set Optimization
Akansha Shukla, Parth Atulbhai Gandhi, Yuval Elovici +1
SIEM systems serve as a critical hub, employing rule-based logic to detect and respond to threats. Redundant or overlapping rules in SIEM systems lead to excessive false alerts, de…
PaniCar: Securing the Perception of Advanced Driving Assistance Systems Against Emergency Vehicle Lighting
Elad Feldman, Jacob Shams, Dudi Biton +7
The safety of autonomous cars has come under scrutiny in recent years, especially after 16 documented incidents involving Teslas (with autopilot engaged) crashing into parked emerg…
Rogue Cell: Adversarial Attack and Defense in Untrusted O-RAN Setup Exploiting the Traffic Steering xApp
Eran Aizikovich, Dudu Mimran, Edita Grolman +2
The Open Radio Access Network (O-RAN) architecture is revolutionizing cellular networks with its open, multi-vendor design and AI-driven management, aiming to enhance flexibility a…
SHIELD: APT Detection and Intelligent Explanation Using LLM
Parth Atulbhai Gandhi, Prasanna N. Wudali, Yonatan Amaru +2
Advanced persistent threats (APTs) are sophisticated cyber attacks that can remain undetected for extended periods, making their mitigation particularly challenging. Given their pe…
Rule-ATT&CK Mapper (RAM): Mapping SIEM Rules to TTPs Using LLMs
Prasanna N. Wudali, Moshe Kravchik, Ehud Malul +3
The growing frequency of cyberattacks has heightened the demand for accurate and efficient threat detection systems. SIEM platforms are important for analyzing log data and detecti…
A Privacy Enhancing Technique to Evade Detection by Street Video Cameras Without Using Adversarial Accessories
Jacob Shams, Ben Nassi, Satoru Koda +2
In this paper, we propose a privacy-enhancing technique leveraging an inherent property of automatic pedestrian detection algorithms, namely, that the training of deep neural netwo…
Towards an End-to-End (E2E) Adversarial Learning and Application in the Physical World
Dudi Biton, Jacob Shams, Satoru Koda +3
The traditional learning process of patch-based adversarial attacks, conducted in the digital domain and then applied in the physical domain (e.g., via printed stickers), may suffe…
UEFI Memory Forensics: A Framework for UEFI Threat Analysis
Kalanit Suzan Segal, Hadar Cochavi Gorelik, Oleg Brodt +3
Modern computing systems rely on the Unified Extensible Firmware Interface (UEFI), which has replaced the legacy Basic Input/Output System (BIOS) as the firmware standard for the m…
Tag&Tab: Pretraining Data Detection in Large Language Models Using Keyword-Based Membership Inference Attack
Sagiv Antebi, Edan Habler, Asaf Shabtai +1
Large language models (LLMs) have become essential tools for digital task assistance. Their training relies heavily on the collection of vast amounts of data, which may include cop…