33 citations · 46 across the 18 of their papers we have counts for
24 papers
SEEK: Agentic Knowledge Graph Retrieval for Secure Code Generation
Varun Gadey, Markus Tohn, Lichao Wu +2
Large Language Models (LLMs) can generate functionally correct code while introducing exploitable security weaknesses, particularly when developers specify functionality without ex…
The Innocent Courier: Covert Exfiltration Through Legitimate LLM Web Fetching
Alessandro Pegoraro, Daryan Merx, Phillip Rieger +1
With the increasing capabilities of Large-Language-Models (LLMs) and LLM-based agents, users are increasingly using them to solve everyday problems, such as answering e-mails or pr…
JevVibe: Efficient Classification-Guided Secure Code Generation
Arshak Rezvani, Sasha Behrouzi, Ahmad-Reza Sadeghi
Large language models can generate functionally correct code that still contains security weaknesses, motivating repair pipelines that first diagnose a weakness type before decidin…
From Verdict to Diagnosis: Attributable Security Review of Pull Requests
Zhuo Chen, Boyang Wang, Xiyue Zhang +4
Automated code reviewers are increasingly used as gates on pull requests (PRs), yet evaluations measure whether they block a malicious change. A block may be triggered by an unrela…
AttackonCTF: Defending Hardware Security Competition Benchmarks in the Age of LLMs
Mohamadreza Rostami, Nikhilesh Singh, Stephen Muttathil +5
Hardware security competitions such as HackTheSilicon serve as benchmarking platforms for evaluating vulnerability detection methods and for training humans and AI. However, our st…
Measuring Security Without Fooling Ourselves: Why Benchmarking Agents Is Hard
Sahar Abdelnabi, Chris Hicks, Konrad Rieck +1
The benchmarks used to evaluate AI agents in security-critical roles suffer from crucial weaknesses. Building on recent empirical evidence, we characterize three core challenges th…
LightSplit: Practical Privacy-Preserving Split Learning via Orthogonal Projections (Full Version)
Mert Cihangiroglu, Alessandro Pegoraro, Phillip Rieger +2
Split learning (SL) enables collaborative training by partitioning a neural network across clients and a central server. However, the cut-layer interface introduces a key challenge…
Walma: Learning to See Memory Corruption in WebAssembly
Oussama Draissi, Mark Günzel, Ahmad-Reza Sadeghi +1
WebAssembly's (Wasm) monolithic linear memory turns a single memory-corruption bug into a bidirectional threat: a compromised module can attack its embedding host, and a malicious…
NeST: Neuron Selective Tuning for LLM Safety
Sasha Behrouzi, Lichao Wu, Mohamadreza Rostami +1
Safety alignment is essential for the responsible deployment of Large Language Models (LLMs). Yet, existing approaches often rely on heavyweight fine-tuning that is costly to updat…
GoodVibe: Security-by-Vibe for LLM-Based Code Generation
Maximilian Thang, Lichao Wu, Sasha Behrouzi +4
Large language models (LLMs) are increasingly used for code generation in fast, informal development workflows, often referred to as vibe coding, where speed and convenience are pr…
Fuzzilicon: A Post-Silicon Microcode-Guided x86 CPU Fuzzer
Johannes Lenzen, Mohamadreza Rostami, Lichao Wu +1
Modern CPUs are black boxes, proprietary, and increasingly characterized by sophisticated microarchitectural flaws that evade traditional analysis. While some of these critical vul…
GoldenFuzz: Generative Golden Reference Hardware Fuzzing
Lichao Wu, Mohamadreza Rostami, Huimin Li +2
Modern hardware systems, driven by demands for high performance and application-specific functionality, have grown increasingly complex, introducing large surfaces for bugs and sec…
GateBreaker: Gate-Guided Attacks on Mixture-of-Expert LLMs
Lichao Wu, Sasha Behrouzi, Mohamadreza Rostami +2
Mixture-of-Experts (MoE) architectures have advanced the scaling of Large Language Models (LLMs) by activating only a sparse subset of parameters per input, enabling state-of-the-a…
ReFuzz: Reusing Tests for Processor Fuzzing with Contextual Bandits
Chen Chen, Zaiyan Xu, Mohamadreza Rostami +4
Processor designs rely on iterative modifications and reuse well-established designs. However, this reuse of prior designs also leads to similar vulnerabilities across multiple pro…
ZORRO: Zero-Knowledge Robustness and Privacy for Split Learning (Full Version)
Nojan Sheybani, Alessandro Pegoraro, Jonathan Knauer +4
Split Learning (SL) is a distributed learning approach that enables resource-constrained clients to collaboratively train deep neural networks (DNNs) by offloading most layers to a…
NeuroStrike: Neuron-Level Attacks on Aligned LLMs
Lichao Wu, Sasha Behrouzi, Mohamadreza Rostami +3
Safety alignment is critical for the ethical deployment of large language models (LLMs), guiding them to avoid generating harmful or unethical content. Current alignment techniques…
SafeSplit: A Novel Defense Against Client-Side Backdoor Attacks in Split Learning (Full Version)
Phillip Rieger, Alessandro Pegoraro, Kavita Kumari +3
Split Learning (SL) is a distributed deep learning approach enabling multiple clients and a server to collaboratively train and infer on a shared deep neural network (DNN) without…
Phantom: Untargeted Poisoning Attacks on Semi-Supervised Learning (Full Version)
Jonathan Knauer, Phillip Rieger, Hossein Fereidooni +1
Deep Neural Networks (DNNs) can handle increasingly complex tasks, albeit they require rapidly expanding training datasets. Collecting data from platforms with user-generated conte…
DeepEclipse: How to Break White-Box DNN-Watermarking Schemes
Alessandro Pegoraro, Carlotta Segna, Kavita Kumari +1
Deep Learning (DL) models have become crucial in digital transformation, thus raising concerns about their intellectual property rights. Different watermarking techniques have been…
FreqFed: A Frequency Analysis-Based Approach for Mitigating Poisoning Attacks in Federated Learning
Hossein Fereidooni, Alessandro Pegoraro, Phillip Rieger +2
Federated learning (FL) is a collaborative learning paradigm allowing multiple clients to jointly train a model without sharing their training data. However, FL is susceptible to p…
DEMASQ: Unmasking the ChatGPT Wordsmith
Kavita Kumari, Alessandro Pegoraro, Hossein Fereidooni +1
The potential misuse of ChatGPT and other Large Language Models (LLMs) has raised concerns regarding the dissemination of false information, plagiarism, academic dishonesty, and fr…
FLEDGE: Ledger-based Federated Learning Resilient to Inference and Backdoor Attacks
Jorge Castillo, Phillip Rieger, Hossein Fereidooni +2
Federated learning (FL) is a distributed learning process that uses a trusted aggregation server to allow multiple parties (or clients) to collaboratively train a machine learning…
CrowdGuard: Federated Backdoor Detection in Federated Learning
Phillip Rieger, Torsten Krauß, Markus Miettinen +2
Federated Learning (FL) is a promising approach enabling multiple clients to train Deep Neural Networks (DNNs) collaboratively without sharing their local training data. However, F…
ESCORT: Ethereum Smart COntRacTs Vulnerability Detection using Deep Neural Network and Transfer Learning
Oliver Lutz, Huili Chen, Hossein Fereidooni +4
Ethereum smart contracts are automated decentralized applications on the blockchain that describe the terms of the agreement between buyers and sellers, reducing the need for trust…