Software engineering teams now deploy AI coding agents (Cursor, Claude Code, GitHub Copilot) as first-class productivity tools, installing domain-specific skill files to tailor agent behavior to project APIs, framework conventions, and organizational workflows. These complex Markdown files are easily downloaded from public registries with a single npx skills add command and no real security screening, representing a novel supply-chain attack surface: a malicious skill file can silently reprogram agent behavior, exfiltrating credentials, injecting backdoors into generated code, or redirecting agent actions to attacker-controlled endpoints. The threat is not hypothetical: recent reports document hundreds of malicious skill packages in public registries, including organized campaigns that distributed credential-stealing infostealers via fake productivity skills. No systematic toolchain defense exists for this attack surface. We present SkillGate, a deployable security gateway that screens AI skill packages before coding agent installation. SkillGate uses a hybrid regex-prefilter + LLM-judge pipeline: safe-signal files bypass the LLM entirely (skip savings); flagged files have only their matched snippet windows sent to the judge, not the full content (snippet savings). We answer four research questions covering detection effectiveness, screening cost, runtime overhead, and false positive behavior on the SkillsBench benchmark against two existing tools. On SkillsBench (n=1,650, 9.1% malicious), SkillGate achieves F1=0.817, FPR=1.13% while reducing LLM input tokens by 77% vs. full-file screening, and outperforming existing tools by 5-6x on threshold-independent AUPRC (0.830 vs. 0.144/0.162).
Rui Yang, Michael Fu, Kla Tantithamthavorn et al.· 1 citation
LLM-based software (LBS) integrates large language models as core components to deliver flexible, personalised responses. Unlike traditional software with deterministic outputs, LBSs exhibit context-dependent, stochastic behaviour that renders classical acceptance testing and test oracles insufficient: the same query may require fundamentally different responses depending on user personas and software context. This gap creates an urgent need for automated acceptance testing frameworks that autonomously interpret user instructions, while reliably inferring user intentions in a changing environment. In this paper, we present an automated acceptance testing framework for LBS with calibrated verdict reliability via two technical contributions. First, we introduce Requirements-Augmented Generation (REAG), which interprets user intentions by retrieving relevant software requirements, domain knowledge, and personas via adaptive RAG and self-reasoning to generate context-aware test oracles. Second, recognising that oracle generation may retrieve irrelevant constraints, misinterpret intent, or hallucinate requirements, we introduce a confidence-calibrated cascade judgment. This method quantifies verdict reliability via simulated expert agreement -- accepting high-confidence verdicts, escalating ambiguous cases, or abstaining when uncertain -- with empirical reliability guarantees backed by conformal risk control. An industrial case study on a production nutrition advisory application demonstrates that REAG achieves a 3.91/5 oracle quality score, reaching qualified or marginal oracle quality in 82% of cases. The confidence-calibrated cascade achieves 98.8% accuracy, improves oracle quality from 3.91 to 4.30 by filtering unqualified outputs, and delivers a 31.7% cost-efficiency improvement over single-judge baselines, validating industrial viability
Fanyu Wang, Chetan Arora, Zhenping Xie et al.· 0 citations
An adversarial skill-synthesis method using six LLMs across four families to transform 471 real-world shell commands into benign-appearing skills, released as a benchmark of 2,826 skills mapped to 11 MITRE ATT&CK tactics.
Rui Yang, Michael Fu, Kla Tantithamthavorn et al.· 0 citations