--- title: we have a year to fix security everywhere | daily.dev description: An open-weight LLM release (GLM 5.3-flash) can be run cheaply on consumer hardware and, once 'abliterated' to remove safety refusals, scores extremely high on... canonical: https://daily.dev/posts/we-have-a-year-to-fix-security-everywhere-duevtor0r twitter:card: summary_large_image twitter:site: @dailydotdev og:type: website og:site_name: daily.dev og:title: we have a year to fix security everywhere | daily.dev og:description: An open-weight LLM release (GLM 5.3-flash) can be run cheaply on consumer hardware and, once 'abliterated' to remove safety refusals, scores extremely high on... og:url: https://daily.dev/posts/we-have-a-year-to-fix-security-everywhere-duevtor0r og:image: https://api.daily.dev/og/posts/DUeVtOR0r.png og:image:alt: we have a year to fix security everywhere og:image:width: 1200 og:image:height: 630 og:locale: en --- > ## Documentation Index > Fetch the complete documentation index at: https://daily.dev/llms.txt > Use this file to discover all available pages before exploring further. # we have a year to fix security everywhere **[Tech Lead Digest](https://daily.dev/sources/techleaddigest)** · 14 min read · 0 upvotes · 0 comments ## Summary An open-weight LLM release (GLM 5.3-flash) can be run cheaply on consumer hardware and, once 'abliterated' to remove safety refusals, scores extremely high on cybersecurity exploitation benchmarks like CyberGym and ExploitBench. This makes automated, round-the-clock vulnerability discovery and exploitation accessible to anyone with a few thousand dollars in hardware. The piece argues the industry has roughly a year before even more capable open models close the gap with frontier labs, and that the bottleneck isn't finding vulnerabilities (LLMs are already good at that) but deploying fixes across legacy and critical infrastructure. It calls for governments to mandate frequent penetration testing, faster patch deployment, and funding for security engineering (especially at hospitals, banks, power utilities, and telecoms), while cautioning against banning models or GPUs as ineffective. Companies are urged to use frontier models for defensive triage, sandbox AI agents with scoped credentials, invest in memory-safe languages, fuzzing, and formal verification, and dramatically shorten the time between patch and deployment. ## Full article daily.dev links to this article rather than hosting it. Read it at the original source: ## Questions this post answers ### What is GLM 5.3-flash and why is it considered a security risk? GLM 5.3-flash is an open-weight large language model from Z.ai (formerly Zhipu AI) that can run on consumer hardware costing around 5-15k USD and achieves roughly 20-45 tokens per second depending on the GPU. Because it is open-weight, third parties like DeAlignAI release 'abliterated' versions with safety refusals stripped out, scoring 0% on Harmbench-320, meaning the model will attempt disinformation, cybercrime, and other malicious tasks without restriction. _Security teams tracking risks from open-weight models can follow model releases and their implications on daily.dev._ ### How well do current AI models perform at finding and exploiting real security vulnerabilities? GLM 5.3 scores 84.5% on CyberGym, a benchmark of real-world vulnerabilities previously found and patched in open source projects, meaning it could reproduce the vast majority of those bugs from public source code and CVE descriptions alone. It also scores 54.4% on ExploitBench, which measures actual exploitation ability up to arbitrary code execution, with GPT-6 Astra reaching 100% and GPT-5.6 Sol scoring 78.5% on that same benchmark. _Developers evaluating how AI models affect their threat model can follow benchmark results on daily.dev._ ### What can organizations do to prepare for AI-accelerated vulnerability exploitation? Organizations should shift investment from finding vulnerabilities, which AI now does cheaply, toward triaging, patching, and deploying fixes quickly, since deployment across legacy and critical systems is the real bottleneck. Recommended steps include sandboxing AI agents with narrowly scoped credentials, blocking requests at the firewall level rather than relying on local filtering, investing in fuzzing and memory-safe languages, and shortening the time between a patch's release and its adoption. _Teams building AI-assisted patch and deployment pipelines can track security engineering practices on daily.dev._ ## Community take How the wider developer community reacted, aggregated from 3 discussions and 760 comments across lobsters, hackernews (as of 2026-09-20). **TL;DR:** The community largely treats the post's cybersecurity urgency as a jumping-off point for broader debates about AI risk, bioweapons, and societal collapse, with significant skepticism about the severity of near-term LLM-enabled threats but genuine concern about the attacker/defender asymmetry that cheap open models create. **Sentiment:** 15% positive · 30% mixed · 55% skeptical **The case for** - LLMs genuinely worsen the attacker/defender asymmetry because attackers only need to succeed once while defenders must be right every time, and AI dramatically lowers the skill floor for attackers. - Cheap, locally-run open models could enable persistent, automated vulnerability scanning and exploitation that was previously out of reach for most individuals. - The collation and dynamic tailoring that LLMs provide meaningfully amplifies novice capability in broad domains like software exploitation, even if narrow domains (pipe bombs) see little uplift. **The pushback** - Many commenters argue the threat is overstated, pointing out that dangerous information has long been freely available without causing widespread harm. - The defender side faces bureaucratic friction, approval chains, and legal constraints that prevent AI from being deployed as freely on defense as on offense, but this asymmetry predates LLMs. - Several commenters note that the hardware cost to run a truly capable open model is still in the $10k–$20k range, limiting the immediate accessibility claim. - The discussion drifts heavily into speculative territory (bioweapons, superintelligence, Fermi paradox) that the post itself doesn't address, suggesting the concrete security argument isn't fully landing. **By community** - lobsters (mixed): No comments were posted, so no community signal is available beyond the 25 upvotes. - hackernews (mixed): The high-point thread is lively but spends most of its energy on tangential debates about AI existential risk, bioweapons, and civil liberties rather than engaging directly with the post's patch-deployment argument, with roughly equal camps of concern and skepticism. **Hottest debate:** Whether cheap open-weight LLMs meaningfully lower the barrier to catastrophic attacks or whether determined bad actors already had access to the necessary knowledge and resources without AI assistance. **Open questions** - How quickly will hardware costs drop to the point where a truly dangerous open model is accessible on consumer-grade devices? - If defenders face approval chains and legal constraints that attackers don't, what structural changes could actually close that gap? - Does the attacker/defender asymmetry in cybersecurity fundamentally worsen with AI, or does AI strengthen both sides proportionally? **Highlights** > Any sufficiently determined individual can buy mac mini, put it under their bed, configure outside proxy via some random internet address and prompt "iterate on websites in the CT logs, one by one, try to find vulnerabilities, if you did - encrypt their data and blackmail them for this bitcoin address". And it'll work, day and night. Abliterated GLM 5.3 is much smarter than average software developer, they know a lot about information security, they can use any available exploits, they can find novel vulnerabilities and they won't say "no". This is dangerous for an average IT system which never encountered nothing worse than some wordpress GET requests. It's not the end of the world. But future will be rough. > — [vbezhenar on hackernews · 4 comments](https://news.ycombinator.com/item?id=49608279) > One argument for LLMs is that although all information on topics X, Y and Z was already available somewhere, LLMs make that information more exploitable through collation, filtering and dynamic tailoring. For a relatively narrow subject area (e.g. construction of pipe bombs) the collation is minimal, and so the filtering and tailoring probably isn't that important; a novice doesn't learn a lot more from the LLM than they would have done from a few Google searches. For a broad subject (practical creation and exploitation of software vulnerabilities), the collation is very significant and the filtering means that LLMs can empower a novice to act at a similar level as an expert. > — [gnfargbl on hackernews](https://news.ycombinator.com/item?id=49607370) > No because on the defense side you need multiple layers of approvals to change anything. If not you have an LLM making production changes that can make the posture worse, or take down services, which is also bad. Once a vulnerability is discovered however if it's in your own software a patch has to be written (without reducing functionality in most cases), tested, and deployed. At every step there will be others arguing about whether this line could do better, my service requires this thing that isn't included. So at every step the patch can be delayed. And if it is someone else's software you will be lucky if it's open source and you can write a patch yourself. If it's closed source or a vendor you have to completely rely on them and use whatever your account rep can pull. Attackers have a massive advantage with AI, partially because the defensive side doesn't want to make their side worse by giving a ln LLM admin access to all their data > — [kswagage on hackernews](https://news.ycombinator.com/item?id=49608563) > Yes, and... Not only the quantity of people who have the minimum aptitude required, specialized expertise requires both knowledge and experience doing these tasks. Using an LLM requires neither. Leaning on an LLM to do much or all of this means it can happen in seconds/minutes/hours, the LLM can do it several times during that psychotic break (as opposed to a fraction of a hack in a single episode). The barrier to entry pre-LLM was both high and the population who could pull it off (before the Chinese/Russians turned this into commerce) was low. Hacking is an VERY asymmetric activity (the attacker only needs to "be right" once, whereas the defender has to be right every time for every asset they defend). It takes geometrically / exponentially more work to defend (while keeping high availability) than it does to defend. The more widespread tools to find vulns / generate exploits are, the faster the posture of the defense side falls from "maybe we can stop most hacks" to "we know we will fail to prevent most breaches, so we need to prioritize securing only the most valuable resources". That's a BAD place for the average company to be in. > — [thephyber on hackernews](https://news.ycombinator.com/item?id=49608924) > Yes but at least with cybersecurity it does not only benefit attackers. Defenders also benefit greatly from AI. There is the worry of the old saying "they (the attackers) only have to succeed once to win, we (the defenders) only have to fail once to lose.". In that sense there is a big imbalance, but the emergence of AI does not really affect that because it strengthens both sides. With physical security like things like pipe bombs that's a lot more imbalanced. However what can we do? The only effective measures include monitoring everyone which is not a solution because it will make the world not worth living in. > — [wolvoleo on hackernews](https://news.ycombinator.com/item?id=49607319) **Source threads** - [lobsters](https://lobste.rs/s/re9wk8/we_have_year_fix_security_everywhere) · 25 points · 24 comments - [hackernews](https://news.ycombinator.com/item?id=49605691) · 312 points · 736 comments - [hackernews](https://news.ycombinator.com/item?id=49572569) · 3 points · 0 comments ## Similar posts on daily.dev - [Vulnerability Research Is Cooked \(sockpuppet.org\)](https://daily.dev/posts/vulnerability-research-is-cooked-sockpuppet-org--b9w6u8pbj) · LWN.net · 0 upvotes · 0 comments --- Tags: [#security](https://daily.dev/tags/security), [#open-source](https://daily.dev/tags/open-source), [#llm](https://daily.dev/tags/llm), [#ai-security](https://daily.dev/tags/ai-security) [View this post on daily.dev](https://daily.dev/posts/we-have-a-year-to-fix-security-everywhere-duevtor0r) ```json {"@context":"https://schema.org","@graph":[{"@type":"Organization","@id":"https://daily.dev/#organization","name":"daily.dev","url":"https://daily.dev","logo":{"@type":"ImageObject","url":"https://daily.dev/apple-touch-icon.png","width":180,"height":180},"sameAs":["https://twitter.com/dailydotdev","https://github.com/dailydotdev","https://www.linkedin.com/company/daily-dev-ltd"]},{"@type":"WebSite","@id":"https://daily.dev/#website","url":"https://daily.dev","name":"daily.dev","publisher":{"@id":"https://daily.dev/#organization"},"potentialAction":{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https://daily.dev/search?q={search_term_string}"},"query-input":"required name=search_term_string"}}]} {"@context":"https://schema.org","@type":"TechArticle","headline":"we have a year to fix security everywhere","url":"https://daily.dev/posts/we-have-a-year-to-fix-security-everywhere-duevtor0r","mainEntityOfPage":{"@type":"WebPage","@id":"https://daily.dev/posts/we-have-a-year-to-fix-security-everywhere-duevtor0r"},"datePublished":"2026-09-13T21:43:36.442Z","dateModified":"2026-09-20T04:29:36.129Z","description":"An open-weight LLM release (GLM 5.3-flash) can be run cheaply on consumer hardware and, once 'abliterated' to remove safety refusals, scores extremely high on...","image":"https://media.daily.dev/image/upload/s--P4t4XyoV--/f_auto/v1722860399/public/Placeholder%2001","thumbnailUrl":"https://media.daily.dev/image/upload/s--P4t4XyoV--/f_auto/v1722860399/public/Placeholder%2001","isAccessibleForFree":true,"articleSection":"Tech Lead Digest","inLanguage":"en","publisher":{"@type":"Organization","name":"daily.dev","url":"https://daily.dev","logo":{"@type":"ImageObject","url":"https://daily.dev/apple-touch-icon.png","width":180,"height":180}},"author":{"@type":"Organization","name":"Tech Lead Digest","logo":"https://media.daily.dev/image/upload/t_logo,f_auto/v1/logos/6ed1ee1a2e5c49f698265a6b2912b80d","url":"https://daily.dev/sources/techleaddigest"},"commentCount":0,"discussionUrl":"https://daily.dev/posts/we-have-a-year-to-fix-security-everywhere-duevtor0r","interactionStatistic":[{"@type":"InteractionCounter","interactionType":{"@type":"LikeAction"},"userInteractionCount":0},{"@type":"InteractionCounter","interactionType":{"@type":"CommentAction"},"userInteractionCount":0}],"keywords":"security,open-source,llm,ai-security","timeRequired":"PT14M"} {"@context":"https://schema.org","@type":"BreadcrumbList","itemListElement":[{"@type":"ListItem","position":1,"name":"Home","item":"https://daily.dev"},{"@type":"ListItem","position":2,"name":"Tech Lead Digest","item":"https://daily.dev/sources/techleaddigest"},{"@type":"ListItem","position":3,"name":"we have a year to fix security everywhere"}]} {"@context":"https://schema.org","@type":"FAQPage","@id":"https://daily.dev/posts/we-have-a-year-to-fix-security-everywhere-duevtor0r#faq","mainEntity":[{"@type":"Question","name":"What is GLM 5.3-flash and why is it considered a security risk?","acceptedAnswer":{"@type":"Answer","text":"GLM 5.3-flash is an open-weight large language model from Z.ai (formerly Zhipu AI) that can run on consumer hardware costing around 5-15k USD and achieves roughly 20-45 tokens per second depending on the GPU. 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