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20192025
most citedNot what you've signed up for: Compromising Real-World LLM-Integrated Applications with Indirect Prompt Injection

46 citations · 66 across the 16 of their papers we have counts for

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Showing 2024Show all

5 papers · 1 filter

cs.LG2024

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…

cs.CR2024

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…

cs.CR2024

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…

cs.LG2024

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…

cs.CL2024

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…