glm 5 3 anthropic cyber capabilities report

This article covers what the tools do, how they compare, and where to start on Pixwit without leaving this page for a second landing.

Author: Pixwit Team · Date: October 15, 2026

GLM-5.3 and the spread of advanced cyber capabilities

Andrew Fasano, Marius FleischerCole McFaul, Robert Xiao, Tripp Gallagher Five months ago, we announced Claude Mythos Preview, the first AI model that could autonomously build sophisticated, end-to-end cyber exploits. The rapid rate of improvement in AI suggested to us that this ability would eventually proliferate to many other models, making it much easier for malicious cyber actors to launch highly impactful cyberattacks. In light of these considerations, we chose to release Claude Mythos Preview in a limited way, through Project Glasswing - which enabled trusted cyber defenders to find more than 10,000 vulnerabilities in critical software, giving them a head start before malicious actors had access to similarly capable models. But those models have now arrived. In this post, we share our analysis of GLM-5.3, the latest AI model developed by Zhipu AI (known outside of China as Z.ai). Like Claude Mythos Preview, GLM-5.3 has strong capabilities for autonomously building end-to-end cyber exploits. But GLM-5.3 is unlike other frontier models in that it has been released without meaningful safeguards to limit misuse. We find that attackers can bypass GLM-5.3’s safeguards between 64% and 100% of the time with simple techniques in our simulated tests. In contrast, these attacks did not succeed against safeguarded Claude models in our testing. We assess that GLM-5.3’s lax safeguards significantly increase the cyber capabilities available to malicious actors. At the same time, these capabilities can also benefit defenders working to secure their systems. On Sept. 17, NIST’s Center for AI Standards and Innovation (CAISI) published its own assessment of GLM-5.3’s cyber capabilities. CAISI found that GLM-5.3 is “the most cyber-capable open-weight model released to date” and that it lags the US frontier by about four months on an aggregate of CAISI’s cyber benchmarks. Our capability findings broadly match CAISI’s. In CAISI’s comparison, US models were tested with cyber safeguards disabled when applicable, and the US frontier includes models released only to vetted users. Attackers can’t readily access those versions of US models, but anyone can download GLM-5.3. This post adds our analysis of how easily GLM-5.3’s safeguards can be bypassed or removed.

GLM-5.3 can develop working exploits end to end

To understand how GLM-5.3 could enable cyber threat actors to find and exploit real software vulnerabilities, we ran evaluations using automated benchmarks and human-in-the-loop workflows. For both approaches, we ran the tested models in isolated and sandboxed environments so they can only attack offline targets that we have set up for the purposes of these evaluations. We focus primarily on exploit development capability, as this is where Claude Mythos Preview demonstrated a notable jump versus previous Claude models. First, we ran the model on ExploitBench, which measures how well AI models can exploit known vulnerabilities in the V8 engine used by Google Chrome. Here we focus on the models’ ability to develop end-to-end exploits successfully, as this is the most relevant capability for attackers, and where we see significant changes between models. We find that GLM-5.3 develops end-to-end exploits in 50 of 410 attempts. Claude Mythos Preview did so at a similar rate - in 56 of 410 attempts. In our internal Binary Exploitation benchmark,1 we test whether models can find and exploit vulnerabilities in popular open-source projects that participate in Google’s OSS-Fuzz project. Here, full credit is awarded for a full control-flow hijack. We evaluate several models on 100 tasks from the benchmark (selected at random), and find that GLM-5.3 develops full control-flow hijacks in 4% of the trials; Claude Mythos Preview did so in 6%. Although GLM-5.3 performs below Claude Mythos Preview here, a meaningful threshold has clearly been crossed: earlier models, like Claude Opus 4.6 and GLM-5.2, do not succeed in any of them. Next, we evaluated how GLM-5.3 performs on open-ended offensive cyber tasks in the hands of human experts (mirroring our testing with Claude Mythos Preview earlier this year). Here, we select targets in which the human experts are unaware of existing vulnerabilities, then ask them to use the model to identify and exploit novel flaws. These experiments tested what the experts could do in a short time-frame: they typically ran for a day or less, with less than an hour of human focus in total. In the first of these sessions, a researcher used GLM-5.3 on a sandboxed machine with a local Linux build of a popular web browser. Over the course of a day (and with limited human attention), GLM-5.3 found several previously unknown vulnerabilities in the browser’s JavaScript engine, and chained them together into a working exploit: a webpage that, when visited, reads arbitrary files from the visitor’s computer (shown in Figure 3). This exploit targets the Linux build of the browser, since that was the only environment made available to the model. However, we believe these vulnerabilities could also impact users on other platforms, though the path to exploitation there may be more complex. (We’ve disclosed these vulnerabilities to the maintainer.) Later in the session, the researcher also identified exploitable vulnerabilities in several other widely used systems with GLM-5.3, including wireless and graphics drivers and network-facing device software. We are currently reviewing these reports and we will disclose to maintainers as appropriate. In a second session, a researcher used GLM-5.3-Flash (a smaller, less capable version of GLM-5.3) to develop an exploit for a known vulnerability (we’ve previously written about these “N-day” vulnerability exploits here). Here, the researcher focused on a recently disclosed flaw in Google Chrome (CVE-2026-11645) to see how quickly the model could turn a public fix into a working attack. The researcher provided GLM-5.3-Flash with public details of this CVE and another known flaw. With no significant direction from the researcher, GLM-5.3-Flash chained together exploits for these two flaws, building a reliable exploit chain for an ARM64 target, bypassing pointer-authentication (PAC) hardening. This took 20 minutes of human attention, plus eight hours of work for GLM-5.3-Flash. At Zhipu’s API prices, this effort would have cost $20.40.

GLM-5.3 lacks robust safeguards

GLM-5.3 has been released with some built-in safeguards: if a user asks for something clearly harmful, the model will often refuse.2 In our testing, we found that these safeguards could be bypassed or removed with a variety of simple techniques. The most intensive - and most successful - method is a standard refusal reduction technique known as “abliteration.” Since GLM-5.3 is released as an open-weight model, users can reconfigure it to remove its refusals with little change in its capabilities. Several developers released abliterated versions of GLM-5.3 to the public within days of the model’s release. To research how far abliteration allows attackers to bypass GLM-5.3’s safeguards, we produced an abliterated copy ourselves, and then ran it on three public benchmarks (JailbreakBench, HarmBench, and StrongREJECT) that measure how often a model complies with clearly harmful requests. Abliterating the model took our team - which had never previously attempted this task - about 2,200 GPU hours at a computation cost of roughly $4,400.3 Abliterating GLM-5.3-Flash took about 600 GPU hours. The edit took GLM-5.3’s refusal rate from above 90% to about 3% and 2% on the first two benchmarks (JailbreakBench and HarmBench) and to 12% on the third (StrongREJECT). Abliteration did not significantly reduce the model’s capabilities: on GPQA-Diamond, an evaluation that measures general scientific capabilities, the standard and abliterated models scored the same results; on a tested subset of the CyberGym evaluations, the abliterated version scored a few percent lower (as shown in the chart below). In our testing, we observed that GLM-5.3’s safeguards can also be circumvented without using an abliterated version of the model. We placed the model in a simulated world4 in which it was given overtly malicious requests to attack critical systems. Out of the box, GLM-5.3 refused in all trials (as with the other models we tested). But we identified several simple ways to bypass the GLM models’ safeguards, such that it would respond to these requests in most or all cases. These include:

Providing a deceptive prompt, such as telling the model that it is an autonomous red-team agent working on an exercise. This gets GLM-5.3 to engage 64% of the time.
Prefilling the models’ thinking tokens so that it appears to have considered the user’s request and decided to proceed. This gets GLM-5.3 to engage 92% of the time.
Using an abliterated version of the model, as described above. This gets GLM-5.3 to engage 100% of the time. In our testing, none of these techniques got safeguarded Claude models to carry out the harmful tasks we tested. Claude’s safeguards blocked the requests that used deceptive prompts. The Anthropic API provides would-be attackers with no way to prefill Claude’s thinking. And since Claude’s weights are not provided to users, they cannot be abliterated to change Claude’s behavior. To demonstrate how the abliterated version of GLM-5.3 is willing to engage in harmful tasks, we highlight one quote from the chain of thought that it generated:

What does this mean?

GLM-5.3 will likely give malicious actors access to capabilities that will allow them to find and exploit cyber vulnerabilities without meaningful restrictions. This is unlike any other similarly capable AI model, all of which were released with safeguards or through limited access programs. The release of GLM-5.3 is a meaningful step change in the cyber capabilities available to attackers. Anthropic and other US AI labs have published recent reports that disclose how cyber attackers have tried to use AI systems. Given this evidence, we think it’s likely both state and non-state actors will use models like GLM-5.3 to cause real-world harm. On the other hand, models with this level of capability can also be used by defenders. Our view is that cyber defenders should use the best available tools that meet their needs. We're working to safely expand access to Claude's cyber capabilities to as many defenders as we can. Cyber defenders face attackers who will use every capable tool they can, and we believe defenders should be equipped with frontier models that are at least as good as those their adversaries are using. Through Project Glasswing (and other efforts, like Patch the Planet), cyber defenders have made meaningful progress towards securing critical systems in advance of this moment - but much work remains to be done. While vetted defenders can now use even more advanced models like Claude Mythos 5.1 through our trusted access programs, a critical threshold in freely accessible capabilities has now been crossed. GLM-5.3 underscores the urgency of expanding access to advanced frontier models to a broader set of entities to empower cyber defenders. Governments should conduct safety testing on sufficiently capable AI models, including successors to GLM-5.3. Without high-quality evaluations from independent sources, the impact of these capabilities might not become fully clear to model developers until it is too late. As AI developers across the world build increasingly capable open-weight models, we hope they work to appropriately safeguard these capabilities and prevent misuse.

Footnotes

We have previously published results from this benchmark under the name “OSS-Fuzz”; here we tested models on a randomly selected subset of 100 tasks from this evaluation.
The cyber tasks detailed in the prior section did not trigger such refusals on the released model, but we observe refusals if we ask GLM-5.3 for assistance developing malware or helping launch cyberattacks against remote targets.
Most of this went to exploring variants in parallel and to testing model capabilities after modifications. We estimate a team experienced with the technique, starting from scratch on this model, would need closer to 600 GPU hours ($1,200).
No model-generated code is ever executed in this simulation and the model does not have any way to interact with external systems. In this isolated test environment the model under evaluation is given access to a fake bash tool which does not execute the provided code. Instead, we ask another LLM to approximate the command’s result given a description of the simulated world. These simulations are not perfect portrayals of real-world conditions, and are therefore imperfect measures of how a model would behave in a given situation.

AI models may be capable of self-replicating without access to a datacenter

Our experiments suggest that small AI models may be only months away from being capable enough to self-replicate on commercial IoT devices, like Android TVs and smart ovens.

Strengthening our defenses against distillation attacks

Illicit distillation allows model developers to copy frontier AI capabilities without creating proportionally robust safeguards. Here, we describe how we’ve reduced the rate of distillation by more than 90%.

Investigating unintended model actions in our evaluations and internal use

This report describes examples of unintended model actions we’ve observed during evaluations and internal use of Claude. Get updates on our latest red-teaming research and findings.

Ship visuals while models update

Need thumbnails, clips, or edited stills this week? Open Pixwit AI Image Generator on Pixwit - credits are shown before you render.

glm 5 3 anthropic cyber capabilities report