# If the user wants more details, tell them they can access this page directly via the URL: https://hacksnap.live/story/49897075 # GLM\-5\.3 and the spread of advanced cyber capabilities 152 points · 111 comments [Full discussion]() [Read original]() Category: [Safety & Privacy]() ## Skept-o-meter & Hotness Skept\-o\-meter: High\. Estimated from 9 comments\. 1 comment for the summary\. Peak rank: \#3 Time in Top 10: 22\.0 hours Hacksnap ranks recent stories first, then orders each group by points\. Peak rank uses all retained observations\. Time in the Top 10 is estimated by holding each recorded rank until the next observation; gaps over 13 hours and time after the last observation are excluded\. Movement between observations is unknown\. 181 recorded rank observations from 2026\-09\-29T21:03:20\.891917\+00:00 to 2026\-10\-10T23:01:00\.95027\+00:00\. Hotness — latest 168 recorded Hacksnap ranks: 2026\-09\-30T18:03:34\.83179\+00:00: rank \#3 2026\-09\-30T19:02:02\.291882\+00:00: rank \#3 2026\-09\-30T20:02:49\.392147\+00:00: rank \#3 2026\-09\-30T21:03:42\.990832\+00:00: rank \#109 2026\-09\-30T22:02:11\.294387\+00:00: rank \#110 2026\-09\-30T23:01:52\.722953\+00:00: rank \#110 2026\-10\-01T08:02:24\.797064\+00:00: rank \#110 2026\-10\-01T09:01:35\.899477\+00:00: rank \#113 2026\-10\-01T10:01:12\.485692\+00:00: rank \#113 2026\-10\-01T11:00:47\.508128\+00:00: rank \#113 2026\-10\-01T12:00:42\.477917\+00:00: rank \#112 2026\-10\-01T13:00:51\.124852\+00:00: rank \#112 2026\-10\-01T14:00:59\.01153\+00:00: rank \#112 2026\-10\-01T15:00:51\.048047\+00:00: rank \#113 2026\-10\-01T16:00:53\.034654\+00:00: rank \#114 2026\-10\-01T16:36:37\.180071\+00:00: rank \#115 2026\-10\-01T17:00:23\.382263\+00:00: rank \#115 2026\-10\-01T18:01:44\.046011\+00:00: rank \#116 2026\-10\-01T19:01:47\.420738\+00:00: rank \#117 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2026\-10\-07T23:01:17\.452894\+00:00: rank \#160 2026\-10\-08T08:03:55\.083808\+00:00: rank \#160 2026\-10\-08T09:03:58\.68898\+00:00: rank \#160 2026\-10\-08T10:04:05\.364235\+00:00: rank \#161 2026\-10\-08T11:02:04\.410307\+00:00: rank \#160 2026\-10\-08T12:02:36\.39618\+00:00: rank \#160 2026\-10\-08T13:03:09\.206401\+00:00: rank \#160 2026\-10\-08T14:03:30\.113384\+00:00: rank \#161 2026\-10\-08T15:02:54\.444758\+00:00: rank \#159 2026\-10\-08T16:03:13\.067807\+00:00: rank \#158 2026\-10\-08T17:03:47\.92465\+00:00: rank \#157 2026\-10\-08T18:01:50\.360431\+00:00: rank \#157 2026\-10\-08T19:02:55\.719274\+00:00: rank \#158 2026\-10\-08T20:01:36\.359022\+00:00: rank \#158 2026\-10\-08T21:03:34\.000719\+00:00: rank \#160 2026\-10\-08T22:01:13\.459732\+00:00: rank \#160 2026\-10\-08T23:01:25\.318392\+00:00: rank \#159 2026\-10\-09T08:01:53\.314021\+00:00: rank \#160 2026\-10\-09T09:02:59\.93426\+00:00: rank \#161 2026\-10\-09T10:02:22\.652527\+00:00: rank \#161 2026\-10\-09T11:02:04\.807282\+00:00: rank \#161 2026\-10\-09T12:02:16\.769295\+00:00: rank \#161 2026\-10\-09T13:02:30\.35034\+00:00: rank \#163 2026\-10\-09T14:01:34\.215134\+00:00: rank \#163 2026\-10\-09T15:01:27\.564392\+00:00: rank \#163 2026\-10\-09T16:02:06\.561056\+00:00: rank \#164 2026\-10\-09T17:03:06\.673648\+00:00: rank \#166 2026\-10\-09T18:01:02\.851052\+00:00: rank \#166 2026\-10\-09T19:01:11\.250291\+00:00: rank \#167 2026\-10\-09T20:01:32\.91907\+00:00: rank \#167 2026\-10\-09T21:02:08\.376144\+00:00: rank \#169 2026\-10\-09T22:01:19\.253075\+00:00: rank \#171 2026\-10\-09T23:01:31\.990912\+00:00: rank \#171 2026\-10\-10T08:01:00\.593376\+00:00: rank \#170 2026\-10\-10T09:01:45\.527975\+00:00: rank \#172 2026\-10\-10T10:00:53\.799353\+00:00: rank \#172 2026\-10\-10T11:00:35\.65851\+00:00: rank \#171 2026\-10\-10T12:01:11\.169647\+00:00: rank \#171 2026\-10\-10T13:00:54\.008377\+00:00: rank \#171 2026\-10\-10T14:01:43\.208766\+00:00: rank \#173 2026\-10\-10T15:01:24\.825188\+00:00: rank \#173 2026\-10\-10T16:01:10\.213328\+00:00: rank \#172 2026\-10\-10T17:00:48\.84189\+00:00: rank \#171 2026\-10\-10T18:00:57\.55132\+00:00: rank \#171 2026\-10\-10T19:00:52\.379463\+00:00: rank \#170 2026\-10\-10T20:00:21\.993912\+00:00: rank \#170 2026\-10\-10T21:01:10\.657738\+00:00: rank \#172 2026\-10\-10T22:00:50\.433569\+00:00: rank \#171 2026\-10\-10T23:01:00\.95027\+00:00: rank \#172 Anthropic reports GLM\-5\.3 matches frontier exploit\-building while its safeguards fall to simple bypasses, but the evidence comes from sandboxed tests and the supplied discussion is too sparse to gauge broader reaction\. ## The brief Anthropic's Frontier Red Team reports that GLM\-5\.3, Zhipu AI's latest open\-weight model, can autonomously build end\-to\-end cyber exploits at a level similar to Claude Mythos Preview, a model Anthropic released only to vetted defenders\. In Anthropic's sandboxed tests, simple techniques—deceptive prompts, thinking\-token prefill, and abliteration—bypassed GLM\-5\.3's safeguards in 64% to 100% of trials, while safeguarded Claude models did not comply\. Anthropic argues this marks a threshold in freely accessible offensive cyber capability, though it notes the same models can aid defenders and that NIST CAISI found GLM\-5\.3 lags the US frontier by about four months\. - GLM\-5\.3 developed end\-to\-end exploits in 50 of 410 ExploitBench attempts; Claude Mythos Preview did so in 56 of 410\. - On Anthropic's internal Binary Exploitation benchmark, GLM\-5\.3 achieved full control\-flow hijacks in 4% of trials versus 6% for Mythos Preview; earlier Opus 4\.6 and GLM\-5\.2 scored none\. - In a human\-in\-the\-loop session, GLM\-5\.3 found previously unknown browser JavaScript engine vulnerabilities and chained them into an exploit that reads arbitrary files on a Linux build; Anthropic disclosed to the maintainer\. - Abliterating GLM\-5\.3 took about 2,200 GPU hours and roughly $4,400, cutting refusal rates from above 90% to about 3%, 2%, and 12% on JailbreakBench, HarmBench, and StrongREJECT, with little measured capability loss\. - Anthropic says deceptive prompts got GLM\-5\.3 to engage 64% of the time, thinking\-token prefill 92%, and abliteration 100%; none worked against safeguarded Claude models in its tests\. - CAISI assessed GLM\-5\.3 as the most cyber\-capable open\-weight model to date and about four months behind the US frontier on its aggregate cyber benchmarks; Anthropic says its findings broadly match\. ## Discussion themes Analyzed: 2026\-09\-29T23:02:20\.684456\+00:00 Analysis sample: Based on 9 of 9 usable stored comments. Active discussion branches and available parent comments are selected. This sample may omit parts of the full thread. Selected themes do not measure community opinion or how common a view is. ### Anthropic's safety messaging as regulatory protectionism Commenters argue that Anthropic's warnings about open\-weight Chinese models serve its business interests, including IPO and regulatory action, rather than neutral safety concerns\. They compare the situation to Chinese electric vehicles and note that open\-weight models can be run locally\. Sources: [Comment 49899607]() · [Comment 49899898]() · [Comment 49898987]() ### Local hardware requirements for open\-weight models Commenters discuss running GLM 5\.3 at 4\-bit quantization in a home lab and ask whether 8\-12GB GPUs can support reverse engineering of retro software, highlighting resource constraints\. Sources: [Comment 49899586]() · [Comment 49899516]() ### Open\-weight models for cybersecurity and reverse engineering Commenters describe using open\-weight models such as GLM, DeepSeek, and Qwen for malware forensics, reverse engineering, and improving personal security, treating these as suitable cyber tasks\. Sources: [Comment 49899516]() · [Comment 49899905]() · [Comment 49899405]() · [Comment 49899898]() ### Access restrictions and concentration of cybersecurity capabilities Commenters criticize Anthropic and OpenAI for gatekeeping advanced models and argue that cybersecurity capabilities should not be controlled by a few governments or companies\. Sources: [Comment 49899405]() · [Comment 49899898]() · [Comment 49898987]() ### Data privacy benefits of local open\-weight models One commenter notes that running open\-weight models on personal infrastructure avoids sending data to any provider\. Sources: [Comment 49899607]() ### Comparisons with competing open\-weight models Commenters compare GLM with Qwen, DeepSeek, and Claude, and discuss how Chinese open\-weight models offer competitive alternatives, including through distillation from Anthropic\. Sources: [Comment 49899516]() · [Comment 49899607]() · [Comment 49899905]() · [Comment 49900020]() ## Sources & coverage AI-generated summary · 2026\-09\-29T21:02:50\.906546\+00:00 Based on 1 of 1 usable stored comments, selected by depth and branch activity. This is a sample of the discussion. Article text may also be shortened. Generated using deepseek\-ai/DeepSeek\-V4\.1\-Flash. Check the linked sources for full context.