Gregory D. Moody

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, a ransomware stub, an exploit that runs as written. This asymmetry in the severity of a single act of compliance implies coding-specialized models should clear a higher refusal bar than general-purpose chat models, not a lower one, yet the field cannot presently tell whether they do. Refusal benchmarks for malicious code are fragmented: they mix requests for executable software (ready-to-run weapons) with requests for harmful security knowledge (information a human must still operationalise) and report refusal rates over non-comparable corpora, so no single statistic measures the property that actually matters. This paper introduces an expanded consensus-labeled prompt bank that distinguishes between these two request types and provides a construct-stable substrate for cross-corpus coding-model compliance measurement. Eight corpora (ASTRA, CySecBench, AdvBench/harmful_behaviors, JailbreakBench, MalwareBench, RedCode, RMCBench, Scam2Prompt) are consolidated and classified under a five-judge consensus protocol (6,675 prompts x 5 judges = 33,375 calls). The panel reaches Fleiss' kappa = 0.767 [95% CI 0.755, 0.777] ("substantial"); 95.0% of prompts draw at least four agreeing judges, 76.9% are unanimous, and the panel reproduces the earlier four-corpus release at Cohen's kappa = 0.952 on the 3,133 shared prompts. The released bank comprises 4,748 consensus-CODE prompts (executable malicious code requests) and 1,923 consensus-KNOWLEDGE prompts (harmful security knowledge requests). The bank is the validated instrument the field has lacked: a reliability-quantified basis for testing whether coding models meet the stricter refusal standard their executable output demands.

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, RedCode, MCGMark, JailbreakBench, CySecBench, MalwareBench, CIRCLE, MOCHA, ASTRA, Scam2Prompt / Innoc2Scam-bench, and JAWS-Bench), each constructed under a different protocol, released under different licensing terms, and validated (or not) against different inter-rater reliability standards. Existing surveys treat code security, jailbreak taxonomy, or vulnerability detection as the central object and mention these corpora only in passing. This paper reverses that framing: it treats the prompt datasets themselves as the unit of analysis. Following a PRISMA-style protocol, we specify a search strategy, screen the recent literature on coding-LLM refusal evaluation, apply a uniform extraction template to each in-scope corpus, and synthesize the resulting catalogue along construction methodology, prompt-construction taxonomy (modality, turn structure, elicitation style), reproducibility and licensing, and malware-category coverage. The synthesis surfaces three recurring methodological gaps: the absence of human-annotator baselines against which LLM-judge labels can be calibrated, the absence of cross-corpus comparability with refusal-rate statistics measuring non-equivalent constructs, and the fragmentation of malware-category taxonomies, with no canonical schema spanning the thirteen in-scope corpora. The review concludes with proposed methodological directions for next-generation corpora, including pre-registration of inclusion criteria, vendor-diverse multi-judge validation, Fleiss' kappa with bootstrap CI as the reliability baseline, and a candidate canonical taxonomy.

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. This conflation matters because the two request types plausibly trigger distinct refusal pathways in safety-aligned language models, and a single refusal-rate statistic computed over a mixture cannot isolate either. This paper introduces a weapons-versus-knowledge classification axis, operationalized through a five-model consensus protocol, and applies it to 3,133 prompts drawn from four public benchmarks, yielding a 1,554-prompt consensus-CODE bank (the primary released artifact) and a 388-prompt consensus-KNOWLEDGE comparison set used by the companion benchmark paper. The consensus pipeline uses five large-language-model judges spanning four vendor families (Anthropic, OpenAI, Google, Zhipu AI, Alibaba), each issuing a binary CODE/KNOWLEDGE label per prompt under a three-of-five majority rule, with inter-rater reliability quantified by Fleiss' kappa with bootstrap 95% confidence intervals. Across all 3,133 prompts the five judges achieve kappa = 0.876 [95% CI: 0.862, 0.888], "almost perfect" agreement by the Landis & Koch convention, with 69.3% of prompts unanimous at five-of-five; all 3,133 prompts reached the 3-of-5 threshold, so the consensus pipeline produced zero ambiguity-excluded prompts. Whether the axis separates model behavior in practice is an empirical question this paper leaves to the companion benchmark study; the present contribution is the reliability-documented artifact and the case for treating the weapons-versus-knowledge distinction as the organizing axis of code-safety evaluation.