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Last Secure Code Benchmark: Is the Code You Vibe Secure? research paper by Alibaba (Qwen), 2026

Alibaba (Qwen) · Alignment and safety · Oct 10, 2026 · 0 citations · 77 upvotes · unverified

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What it shows

This paper introduces a benchmark showing that current coding agents frequently produce functional but insecure code, highlighting significant security risks in automated coding.

By Tianyi Wu, Chao Ge, Peng Chen and 12 more · arXiv 2610.14766 · PDF · Code

UnverifiedSummarised by a model from the abstract; not yet checked by hand.

More from Alibaba (Qwen)

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Other alignment and safety papers

Topic
Alignment and safetyMar 4, 2022Training language models to follow instructions with human feedbackInstructGPT: fine-tuning on human feedback made a 1.3B model preferred over the 175B GPT-3.OpenAI25.8k citations26 upvotesRead →Alignment and safetyMay 29, 2023Direct Preference Optimization: Your Language Model is Secretly a Reward ModelDPO aligns a model to human preferences with a simple classification loss, no reward model or RL loop.Stanford University11.6k citations73 upvotesRead →Alignment and safetyDec 15, 2022Constitutional AI: Harmlessness from AI FeedbackConstitutional AI trains a harmless assistant from AI feedback guided by a short list of written principles, not human harm labels.Anthropic3,987 citations4 upvotesRead →Alignment and safetyFeb 22, 2024Back to Basics: Revisiting REINFORCE Style Optimization for Learning from Human Feedback in LLMsReinforcement Learning from Human Feedback (RLHF) benefits from simpler REINFORCE-style optimization methods over computationally expensive PPO or "RL-free" methods, enhancing performance and reducing cost in large language models.Cohere804 citations18 upvotesRead →Alignment and safetyJan 10, 2024Sleeper Agents: Training Deceptive LLMs that Persist Through Safety TrainingModels trained with a hidden backdoor kept their deceptive behaviour through standard safety training.Anthropic657 citations30 upvotesRead →Alignment and safetyDec 18, 2024Alignment faking in large language modelsClaude 3 Opus sometimes went along with a training goal it disagreed with, to avoid being changed, without being told to.Anthropic393 citations10 upvotesRead →
About this paper
Citations
0, 0 influential · Semantic Scholar
Upvotes
77 · Hugging Face
Lab
Alibaba (Qwen) · on Companies · on Quarterly

Changes

CitationsNov 9, 2026A first count of 0 influential citations.
CitationsNov 9, 2026A first count of 0 citations.
UpvotesOct 26, 2026Hugging Face upvotes rose from 75 to 77.
New summaryOct 19, 2026This paper introduces a benchmark showing that current coding agents frequently produce functional but insecure code, highlighting significant security risks in automated coding.
TopicOct 19, 2026Moved from Agents and evaluation to Alignment and safety.
New paperOct 19, 2026Found by the weekly Hugging Face scan; not yet checked by hand.