13 citations · 13 across the 11 of their papers we have counts for
11 papers
Certifiably robust malware detectors by design
Pierre-Francois Gimenez, Sarath Sivaprasad, Mario Fritz
Malware analysis involves analyzing suspicious software to detect malicious payloads. Static malware analysis, which does not require software execution, relies increasingly on mac…
Taxonomy, Opportunities, and Challenges of Representation Engineering for Large Language Models
Jan Wehner, Sahar Abdelnabi, Daniel Tan +2
Representation Engineering (RepE) is a novel paradigm for controlling the behavior of LLMs. Unlike traditional approaches that modify inputs or fine-tune the model, RepE directly m…
COMIX: Compositional Explanations using Prototypes
Sarath Sivaprasad, Dmitry Kangin, Plamen Angelov +1
Aligning machine representations with human understanding is key to improving interpretability of machine learning (ML) models. When classifying a new image, humans often explain t…
Context-Aware Reasoning On Parametric Knowledge for Inferring Causal Variables
Ivaxi Sheth, Sahar Abdelnabi, Mario Fritz
Scientific discovery catalyzes human intellectual advances, driven by the cycle of hypothesis generation, experimental design, evaluation, and assumption refinement. Central to thi…
Dataset and Lessons Learned from the 2024 SaTML LLM Capture-the-Flag Competition
Edoardo Debenedetti, Javier Rando, Daniel Paleka +18
Large language model systems face important security risks from maliciously crafted messages that aim to overwrite the system's original instructions or leak private data. To study…
Get my drift? Catching LLM Task Drift with Activation Deltas
Sahar Abdelnabi, Aideen Fay, Giovanni Cherubin +3
LLMs are commonly used in retrieval-augmented applications to execute user instructions based on data from external sources. For example, modern search engines use LLMs to answer q…
Can LLMs Separate Instructions From Data? And What Do We Even Mean By That?
Egor Zverev, Sahar Abdelnabi, Soroush Tabesh +2
Instruction-tuned Large Language Models (LLMs) show impressive results in numerous practical applications, but they lack essential safety features that are common in other areas of…
LLM Task Interference: An Initial Study on the Impact of Task-Switch in Conversational History
Akash Gupta, Ivaxi Sheth, Vyas Raina +2
With the recent emergence of powerful instruction-tuned large language models (LLMs), various helpful conversational Artificial Intelligence (AI) systems have been deployed across…
A Theory of Response Sampling in LLMs: Part Descriptive and Part Prescriptive
Sarath Sivaprasad, Pramod Kaushik, Sahar Abdelnabi +1
Large Language Models (LLMs) are increasingly utilized in autonomous decision-making, where they sample options from vast action spaces. However, the heuristics that guide this sam…
Don't Miss Out on Novelty: Importance of Novel Features for Deep Anomaly Detection
Sarath Sivaprasad, Mario Fritz
Anomaly Detection (AD) is a critical task that involves identifying observations that do not conform to a learned model of normality. Prior work in deep AD is predominantly based o…
Cooperation, Competition, and Maliciousness: LLM-Stakeholders Interactive Negotiation
Sahar Abdelnabi, Amr Gomaa, Sarath Sivaprasad +2
There is an growing interest in using Large Language Models (LLMs) in multi-agent systems to tackle interactive real-world tasks that require effective collaboration and assessing…