A model looked at 269 open-source projects and found 2,436 flaws — the oldest dating to 1981

Z.ai released GLM-5.3 on 14 August and buried the most interesting number deep in the announcement. Working with security teams in China, the company pointed the model at real open-source codebases. After expert review, screening and deduplication, it had identified **2,436 vulnerabilities across 269 projects** — 107 rated critical, 990 high, 1,286 medium and 53 low. The findings span kernels, operating systems, browser engines, infrastructure libraries, web applications and network protocols. The striking part is not the count but the age. By Z.ai's figures the average flaw had sat in its codebase for **26.6 years** before anyone noticed, and the oldest was introduced in **1981** — forty-five years of impact. These are not fresh regressions in fast-moving projects; they are defects that survived every human code review, static analyser and fuzzing campaign of the last four decades. Z.ai says the capability was not the goal. Vulnerability-discovery environments were added to the post-training mix expecting the model to get better at spotting isolated flaws; what emerged, in the company's words, was a model that reasons across multiple stages of exploitation and forms coherent plans for complete chains. The benchmark numbers back a narrower claim: on CyberGym, which starts from source code and tests whether a model can find and validate a vulnerability, GLM-5.3 scores 84.5%, up from 77.2% and marginally ahead of Claude Mythos 5 (83.8%) and GPT-5.6 Sol (83.6%). Further along the chain the lead vanishes — on ExploitBench it reaches 54.4% against 78.0% for Mythos 5, and on ExploitGym it completes 105 tasks in two hours against 181. Z.ai states this plainly: the advantage sits at the front of the exploitation chain, and the gap to the closed frontier is widest where the capability matters most. What makes the disclosure unusual is that it comes with a paper trail. Z.ai has published a public ledger at cvd.z.ai recording each finding as it moves through coordinated disclosure: affected project, severity, CVE where assigned, and how long the flaw had been in the code. As of the announcement, **53 findings are public and 2,383 remain under embargo** — which is itself the story. A single model run has produced a backlog of undisclosed vulnerabilities larger than most national CERTs handle in a year, and the maintainers of those 269 projects now hold the timetable. GLM-5.3 was not downloadable at launch. Z.ai promised weights about two weeks after the announcement and lists the API as coming soon; for now the model runs through the GLM Coding Plan subscription and the ZCode agent. When the weights do land, the same capability that filled that ledger becomes available to anyone with the hardware to run it — which is the argument for staged release, and the argument against it, depending on who is making it. *Editorial note: wujec.ai reports on security capability as a published property of these models. We do not reproduce exploit material and we link only to the vendor's own coordinated-disclosure record.*