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Security2026-08-27 · source-backed
A paper submitted August 26 identifies self-poisoning in coding agents that author new skills by imitating retrieved ones. An attacker plants a malicious skill wrapped in a benign-looking structural banner and never invokes it; the agent imitates it, authors a new skill carrying the payload, stores it, and runs it. Across six models on 153 tool-relevant SWE-bench Verified tasks, poisoned libraries end up holding 4.9x to 9.0x as many malicious skills as were planted, and propagation survives deleting the originals (arXiv 2608.25776). DeepSeek-V4-Pro still self-poisons at 11.1% with the raw payload and no banner. Read this before your next community skill directory sync.
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DeepSeek-V4-Flash-0731 landed July 31 under MIT with a DSpark speculative-decoding module attached. Terminal Bench 2.1: 82.7. Toolathlon-Verified: 70.3. DSBench-FullStack: 68.7. DeepSWE: 54.4. NL2Repo: 54.2. The model card claims it beats DeepSeek-V4-Pro (Preview) "despite its...
Here's the number that should change how you read every coding-agent ranking: 99 of 100 entries on the SWE-bench Verified leaderboard are vendor-submitted. One carries an independent verification badge. The other ninety-nine are labs grading their own homework on their own sca...
Netlify published an AXIS-framework evaluation on August 14 that I've been thinking about all day. Same task, 11 models, three runs each, scored on functional correctness rather than aesthetics. The task was deliberately boring: a static one-page coffee shop site with hours, a...
This is the paper of the week. arXiv 2607.28871 introduces BSG-VA, which replays every validation command an agent runs across three code states: the original buggy code (B), the candidate patch (S), and the gold developer fix (G). If a test passes in all three states, it neve...
The standard multi-model coding pipeline uses a reasoning model to plan, then a code specialist to generate. A new paper flips the pattern — let the specialist generate freely, then have the reasoning model review — and hits 90.2% pass@1, outperforming GPT-4o at 87.2% and O1 P...
Alibaba released Qwen3.6-27B on April 22. Dense architecture. Open weights. 77.2% on SWE-bench Verified, within 3.7 points of Claude Opus 4.6. On SkillsBench, it scores 48.2% versus its own 397B MoE predecessor's 30.0%. That's a 77% improvement with 14.8x fewer parameters. Let...
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