output
20172026
most citedFactuality Challenges in the Era of Large Language Models

163 citations

Showing 2025Show all

16 papers · 1 filter

cs.CR2025★ 4 cited

Chasing Shadows: Pitfalls in LLM Security Research

Jonathan Evertz, Niklas Risse, Nicolai Neuer +12

Large language models (LLMs) are increasingly prevalent in security research. Their unique characteristics, however, introduce challenges that undermine established paradigms of re…

cs.SE2025

On Interaction Effects in Greybox Fuzzing

Konstantinos Kitsios, Marcel Böhme, Alberto Bacchelli

A greybox fuzzer is an automated software testing tool that generates new test inputs by applying randomly chosen mutators (e.g., flipping a bit or deleting a block of bytes) to a…

quant-ph2025

Computational Monogamy of Entanglement and Non-Interactive Quantum Key Distribution

Alex B. Grilo, Giulio Malavolta, Michael Walter +1

Quantum key distribution (QKD) enables Alice and Bob to exchange a secret key over a public, untrusted quantum channel. Compared to classical key exchange, QKD achieves everlasting…

cs.CR2025★ 3 cited

Protocol-Aware Firmware Rehosting for Effective Fuzzing of Embedded Network Stacks

Moritz Bley, Tobias Scharnowski, Simon Wörner +2

One of the biggest attack surfaces of embedded systems is their network interfaces, which enable communication with other devices. Unlike their general-purpose counterparts, embedd…

cs.CR2025

Empirical Security Analysis of Software-based Fault Isolation through Controlled Fault Injection

Nils Bars, Lukas Bernhard, Moritz Schloegel +1

We use browsers daily to access all sorts of information. Because browsers routinely process scripts, media, and executable code from unknown sources, they form a critical security…

cs.HC2025

Campus AI vs. Commercial AI: Comparing How Students and Employees Perceive their University's LLM Chatbot vs. ChatGPT

Leon Hannig, Annika Bush, Meltem Aksoy +3

As the use of LLM chatbots by students and researchers becomes more prevalent, universities are pressed to develop AI strategies. One strategy that many universities pursue is to c…

cs.HC2025

EchoAid: Enhancing Livestream Shopping Accessibility for the DHH Community

Zeyu Yang, Zheng Wei, Yang Zhang +4

Livestream shopping platforms often overlook the accessibility needs of the Deaf and Hard of Hearing (DHH) community, leading to barriers such as information inaccessibility and ov…

cs.LG2025★ 3 cited

Underrepresentation, Label Bias, and Proxies: Towards Data Bias Profiles for the EU AI Act and Beyond

Marina Ceccon, Giandomenico Cornacchia, Davide Dalle Pezze +2

Undesirable biases encoded in the data are key drivers of algorithmic discrimination. Their importance is widely recognized in the algorithmic fairness literature, as well as legis…

cs.CY2025★ 23 cited

How malicious AI swarms can threaten democracy: The fusion of agentic AI and LLMs marks a new frontier in information warfare

Daniel Thilo Schroeder, Meeyoung Cha, Andrea Baronchelli +19

Advances in AI offer the prospect of manipulating beliefs and behaviors on a population-wide level. Large language models and autonomous agents now let influence campaigns reach un…

cs.CY2025

Campus AI vs Commercial AI: A Late-Breaking Study on How LLM As-A-Service Customizations Shape Trust and Usage Patterns

Leon Hannig, Annika Bush, Meltem Aksoy +2

As the use of Large Language Models (LLMs) by students, lecturers and researchers becomes more prevalent, universities - like other organizations - are pressed to develop coherent…

cs.SE2025

Refining Fuzzed Crashing Inputs for Better Fault Diagnosis

Kieun Kim, Seongmin Lee, Shin Hong

We present DiffMin, a technique that refines a fuzzed crashing input to gain greater similarities to given passing inputs to help developers analyze the crashing input to identify…

cs.CR2025★ 2 cited

"That's another doom I haven't thought about": A User Study on AI Labels as a Safeguard Against Image-Based Misinformation

Sandra Höltervennhoff, Jonas Ricker, Maike M. Raphael +6

As generative AI is increasingly contributing to the spread of deceptively realistic misinformation, lawmakers have introduced regulations requiring the disclosure of AI-generated…

cs.SE2025★ 1 cited

An Empirical Study of Fuzz Harness Degradation

Philipp Görz, Joschua Schilling, Nicolai Bissantz +1

The purpose of continuous fuzzing platforms is to enable fuzzing for software projects via fuzz harnesses -- but as the projects continue to evolve, are these harnesses updated in…

cs.LG2025★ 23 cited

Humanity's Last Exam

Long Phan, Alice Gatti, Ziwen Han +1168

Benchmarks are important tools for tracking the rapid advancements in large language model (LLM) capabilities. However, benchmarks are not keeping pace in difficulty: LLMs now achi…

cs.SE2025★ 1 cited

Evaluating LLM-Based Regression Test Generation

Jing Liu, Seongmin Lee, Eleonora Losiouk +1

Large Language Models (LLMs) have shown tremendous promise in automated software engineering. In this paper, we investigate LLMs for just-in-time regression test generation for pro…

cs.PL2025★ 1 cited

Decompiling for Constant-Time Analysis

Santiago Arranz-Olmos, Gilles Barthe, Lionel Blatter +3

The CT programming discipline is commonly used to protect cryptographic libraries against side-channel attacks. However, it is hard to write CT code; moreover, compilers can introd…