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20172023
most cited(Un)informed Consent: Studying GDPR Consent Notices in the Field

356 citations · 498 across the 21 of their papers we have counts for

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Showing cs.CRShow all

25 papers · 1 filter

cs.CR2023★ 2 cited

EF/CF: High Performance Smart Contract Fuzzing for Exploit Generation

Michael Rodler, David Paaßen, Wenting Li +4

Smart contracts are increasingly being used to manage large numbers of high-value cryptocurrency accounts. There is a strong demand for automated, efficient, and comprehensive meth…

cs.CR2023

No more Reviewer #2: Subverting Automatic Paper-Reviewer Assignment using Adversarial Learning

Thorsten Eisenhofer, Erwin Quiring, Jonas Möller +3

The number of papers submitted to academic conferences is steadily rising in many scientific disciplines. To handle this growth, systems for automatic paper-reviewer assignments ar…

cs.CR2023★ 46 cited

Not what you've signed up for: Compromising Real-World LLM-Integrated Applications with Indirect Prompt Injection

Kai Greshake, Sahar Abdelnabi, Shailesh Mishra +3

Large Language Models (LLMs) are increasingly being integrated into various applications. The functionalities of recent LLMs can be flexibly modulated via natural language prompts.…

cs.CR2023★ 6 cited

CodeLMSec Benchmark: Systematically Evaluating and Finding Security Vulnerabilities in Black-Box Code Language Models

Hossein Hajipour, Keno Hassler, Thorsten Holz +2

Large language models (LLMs) for automatic code generation have achieved breakthroughs in several programming tasks. Their advances in competition-level programming problems have m…

cs.CR2022★ 1 cited

xTag: Mitigating Use-After-Free Vulnerabilities via Software-Based Pointer Tagging on Intel x86-64

Lukas Bernhard, Michael Rodler, Thorsten Holz +1

Memory safety in complex applications implemented in unsafe programming languages such as C/C++ is still an unresolved problem in practice. Many different types of defenses have be…

cs.CR2021

Dompteur: Taming Audio Adversarial Examples

Thorsten Eisenhofer, Lea Schönherr, Joel Frank +3

Adversarial examples seem to be inevitable. These specifically crafted inputs allow attackers to arbitrarily manipulate machine learning systems. Even worse, they often seem harmle…