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R. Young

10 papers hereh-index 319 citations11 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • sole author7
  • first author3

Across the 10 of 10 papers where every author was matched, so the position is known.

fields
  • cs.CL5
  • cs.CR4
  • cs.CV1
same name
  • R. Young — 61 papers, h 22
  • R. Young — 26 papers, h 25
  • R. Young — 15 papers, h 30
  • R. Young — 14 papers, h 86
  • R. Young — 13 papers, h 17
  • R. Young — 11 papers

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

collaborators
Showing cs.CRShow all

4 papers · 1 filter

cs.CR2026

Code as a Weapon: A Consensus-Labeled Prompt Bank for Measuring Coding-Model Compliance with Malicious-Code Requests

Richard J. Young, Gregory D. Moody

A general-purpose language model that answers a harmful question returns text; a coding model that complies with a malicious request can return a working weapon: a keylogger, ranso…

cs.CR2026

Refusal Evaluation in Coding LLMs and Code Agents: A Systematic Review of Thirteen Malicious-Code Prompt Corpora (2023-2025)

Richard J. Young, Gregory D. Moody

The evaluation of large language model refusal on malicious-coding tasks now spans at least thirteen publicly released prompt corpora (AdvBench, the CyberSecEval family, RMCBench,…

cs.CR2026

A Validated Prompt Bank for Malicious Code Generation: Separating Executable Weapons from Security Knowledge in 1,554 Consensus-Labeled Prompts

Richard J. Young, Gregory D. Moody

Existing benchmarks of language-model refusal on malicious-coding tasks routinely conflate requests for executable malicious software with requests for harmful security knowledge.…

cs.CR2025

Evaluating the Robustness of Large Language Model Safety Guardrails Against Adversarial Attacks

Richard J. Young

Large Language Model (LLM) safety guardrail models have emerged as a primary defense mechanism against harmful content generation, yet their robustness against sophisticated advers…

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