4 papers
Independent Clinical Evaluation of General-Purpose LLM Responses to Signals of Suicide Risk
Nick Judd, Alexandre Vaz, Kevin Paeth +5
We introduce findings and methods to facilitate evidence-based discussion about how large language models (LLMs) should behave in response to user signals of risk of suicidal thoug…
SandboxEval: Towards Securing Test Environment for Untrusted Code
Rafiqul Rabin, Jesse Hostetler, Sean McGregor +2
While large language models (LLMs) are powerful assistants in programming tasks, they may also produce malicious code. Testing LLM-generated code therefore poses significant risks…
Malicious and Unintentional Disclosure Risks in Large Language Models for Code Generation
Rafiqul Rabin, Sean McGregor, Nick Judd
This paper explores the risk that a large language model (LLM) trained for code generation on data mined from software repositories will generate content that discloses sensitive i…
To Err is AI : A Case Study Informing LLM Flaw Reporting Practices
Sean McGregor, Allyson Ettinger, Nick Judd +10
In August of 2024, 495 hackers generated evaluations in an open-ended bug bounty targeting the Open Language Model (OLMo) from The Allen Institute for AI. A vendor panel staffed by…