Vectorization via Single Instruction, Multiple Data (SIMD) architectures is a cornerstone of high-performance computing. To fully exploit hardware potential, developers often resort to explicit vectorization using intrinsics, as compiler-based auto-vectorization frequently yields suboptimal results due to conservative static analysis. While Large Language Models (LLMs) have demonstrated remarkable proficiency in general code generation, they struggle with explicit vectorization due to the scarcity of high-quality corpora and the strict semantic constraints of low-level hardware instructions. In this paper, we propose AutoVecCoder, a novel framework designed to empower LLMs with the capability of automated explicit vectorization. AutoVecCoder integrates two core components: VecPrompt, an automated data synthesis pipeline to inject domain-specific intrinsic knowledge; and VecRL, a reinforcement learning framework that aligns code generation with execution efficiency. AutoVecCoder-8B trained by this framework achieves state-of-the-art performance on the SSE and AVX subsets of SimdBench and, in some cases, generates implementations surpassing standard -O3 optimizations, effectively overcoming the inherent bottlenecks of traditional automated vectorization.

Multi-agent LLM systems delegate through natural language, creating trust boundaries that single-agent safety hierarchies were not designed to preserve. We identify provenance erasure as a multi-agent safety failure mechanism: even in the minimal two-agent case, delegation strips trust metadata—98% of format markers, 100% of authority entities, and 44% of source attribution across 460 pairs—while often preserving the actionable payload. This lossy transform enables Trust Cascade, amplifying system-channel attacks from 0% to 33–63% cascade ASR across eight open-weight models; a delegation-as-user control isolates channel trust escalation rather than content reformulation. Because the failure occurs at the delegation boundary, we propose the Trust Boundary Auditor (TBA), a zero-inference provenance diagnostic achieving 86–100% conditional detection with 0% false positives across four domains. Training, scale, and closed-source evaluations show that stronger models can interrupt different cascade stages but do not remove the boundary problem, suggesting that multi-agent safety requires delegation gating plus provenance auditing.

Large language model (LLM) safety is a critical issue, with numerous studies employing red team testing to enhance model security. Among these, jailbreak methods explore potential vulnerabilities by crafting malicious prompts that induce model outputs contrary to safety alignments. Existing black-box jailbreak methods often rely on model feedback, repeatedly submitting queries with detectable malicious instructions during the attack search process. Although these approaches are effective, the attacks may be intercepted by content moderators during the search process. We propose an improved transfer attack method that guides malicious prompt construction by locally training a mirror model of the target black-box model through benign data distillation. This method offers enhanced stealth, as it does not involve submitting identifiable malicious instructions to the target model during the search phase. Our approach achieved a maximum attack success rate of 92%, or a balanced value of 80% with an average of 1.5 detectable jailbreak queries per sample against GPT-3.5 Turbo on a subset of AdvBench. These results underscore the need for more robust defense mechanisms.