Titania Chief Product Officer Ian Robinson understands the anxiety caused by the news that suspected China-backed (APT) groups have deployed open-source AI to hack into Taiwanese government systems. But though this demonstrated the additional capabilities that AI gives attackers, the essential defensive steps remain the same: segment your network, adopt and enforce least privilege access principles and monitor as often as you can.
It’s the news everyone in the network security world feared but secretly expected: an AI tool has found its way through layers of defence to penetrate government systems.
As explained in detail in this Cyber Magazine article, an autonomous attack on Taiwanese infrastructures mapped systems and compromised dozens of user accounts. These resulted in the exfiltration of more than 2500 personnel records, as well as backdoors being installed on government web applications.
And after finishing with the government systems, its next target was the energy sector.
Explainer: What was attacked and why was it different?
But perhaps most alarmingly, the evidence demonstrates that every time systems blocked the attack, the AI tool adapted, applying new techniques and switching its focus to different vulnerabilities.
Built on open-source AI systems and involving eight autonomous agents simultaneously, this tool was described by Israeli AI specialists Dream – who uncovered the attack – as acting like a co-ordinated cyber team.
Fingers point strongly towards Chinese involvement. Taiwan is no stranger to such attacks: the country’s National Security Bureau reported that in 2025, there were more than 2.6 million Chinese cyber attacks on Taiwanese networks every day.
Understandably, government agencies there had implemented a range of security measures, but by repeatedly shifting its attack path, the tool was able to get past many of these.
While this particular attack is now believed to be over, and the additional backdoors it installed closed, governments – as well as many business and critical infrastructure providers – around the world are rightly worried about its ramifications.
The chief strategy officer at Dream, Amir Becker, concluded that the use of AI tools in this way means that governments around the world must now work on the basis that they are under permanent cyber attack.
But alarming as it is to learn that publicly available AI tools are being harnessed in this way, the finer details also reveal some specific shortcomings in the government’s security approach, all of which can be readily remedied.
Firstly, the AI agents were able to work autonomously to crack employee credentials: clearly a cause for concern. However, the impact this had was magnified because these were single sign-on (SSO) creds, allowing the agents to use them as skeleton keys to access different applications and vast areas of the network.
This basically meant there was minimal protection beyond the perimeter. As soon as the agents had breached it, they were able to move laterally across the network without restrictions.
Better network security practice involves segmentation, with additional authentication required to access data and materials.
Ideally, organisations should adopt Least Privilege Access principles, restricting access to each segment to those who specifically need it. That includes at admin-level, where a tiered approach based on role and function is necessary.
A 'deny all, permit by exception' model means that even if an AI agent successfully cracks a password and harvests a legitimate credential, it is then limited to exactly what that user is permitted to see: nothing more. And to go further, it needs a new password, obtained from a different source.
Organisations in many sectors and parts of the world have adopted an increasingly relaxed approach to this, reducing the number of passwords their people have to remember and eliminating the friction of re-entering creds from the everyday user experience.
But when attackers can act with this speed and at this scale, and exploit gaps and vulnerabilities far faster than they can be detected, defence in depth is essential.
What’s needed is a pragmatic balance between the risks of attackers moving laterally across a flat network, and the real impact on productivity of having to recall extra passwords. This is something that network mapping tools like Nipper OmniSight can assist with, providing valuable insights into potential attack paths to inform your segmentation strategy.
It will help you visualise clearly how easy it is for attackers to move from the initial compromise of a password to reaching (e.g.) financial data. That in turn helps build a compelling case for network segmentation best practices and authentication, and the adoption of policies that restrict access to the financial data to those who emphatically need it on an everyday basis.
Of course, many governments have already adopted some form of segmentation; indeed, it’s probable that Taiwan had taken some steps in this direction.
But segmentation is not a “one and done” task. Over time, configurations drift; new devices are added, for instance, without the same policies being enforced, while old devices may be forgotten during patching. Frequently, users are granted additional access for a specific purpose, but there’s no process to ensure that access is then turned off again when they no longer need it.
To address this, network segmentation best practices require regular reassessment of configurations checking whether devices such as routers and firewalls are correctly enforcing the access policies.
This is a key output of a Nipper OmniSight assessment; it flags any deviation from the intended policies, but also risk-prioritises these, to identify the most dangerous exposures. That then allows network security teams to act swiftly to shut down key exposures and update or reapply policies to support network hardening – while offering timely reminders of the need to enforce access rights and shut down forgotten paths.
Finally, one unnerving feature of the attack on Taiwan was the news that the AI tool’s own safety guardrails – designed to stop it being used for malicious hacking – were reportedly bypassed by simply labelling the activity as “authorised penetration testing”.
This serves as a reminder that you can't rely on an agent's internal safeguards alone.
If an activity is unwanted, it needs to be blocked at the network level, regardless of what any application or agent claims it is doing or reports about its own authorisation. And if it does get passed the initial checks, you need to see such activity fast. With Nipper OmniSight (Continuous), you get alerted to every single change to a device on the network, so it’s not AI that’s deciding whether the activity is authorised: it’s the security team.
Discovering that widely available AI tools can be used for coordinated attacks is disconcerting. Learning that hostile states are deploying tools in this way is becoming wearily familiar.
But no government organisation, nor major business or infrastructure provider, should become weary of getting the security foundations in place: segment your network, apply access controls and monitor your network, with a solution like Nipper OmniSight.
Put another way, you don’t need to invest in specialist AI tools to defend against AI-enabled attackers. The best approaches are fundamental policies and practices that every organisation can, and should, adopt.
To find out more about best practice approaches, read our use case on How to limit lateral movement with network segmentation and access control.
Or, to learn more about the capabilities of Nipper OmniSight to guide your segmentation strategy and validate it’s working, book a demo with our team.
Key questionsWhat made the Taiwan cyber-attack different from previous attacks?Apart from the scale of the attack, the big difference is that in this instance, AI agents worked autonomously and collaboratively to get inside government systems. They performed reconnaissance across multiple networks and identify vulnerabilities to pursue multiple attack paths simultaneously. And when paths were shut down, they adapted their attacks. How can a government defend itself against AI attacks like this?Apply defence in depth: use multiple layers and tactics from intrusion detection to network hardening that reduces vulnerabilities. The most effective layer to add is network segmentation, so that even when attackers get through the perimeter defence, they can’t immediately access sensitive data and critical systems. Network segmentation best practices also involve well-enforced access controls that mean only authorised users have access to key segments. These should ideally be based on the principle of Least Privileged Access. How does segmentation help when attackers can use AI and refocus their paths?Network segmentation helps because it puts extra barriers in the way of attackers to stop them moving laterally across the network – whether they’re using AI or not. If there is no physical connection between two segments, an attacker breaching one segment can’t see the other; they’re not even aware it exists. So as a practical step, if two segments should never ever need to communicate with each other, don't create a physical connection between them. Access controls are also vital. If only a small number of users have access to highly sensitive segments, AI will have to work harder to identify them. So segmentation and access controls slow AI down. Was there anything that Taiwan should have done differently?We don’t know exactly what Taiwan’s defences consisted of – but we do know that there were enough defences to make the AI tools refocus their attacks on several occasions. One issue that organisations may want to rethink is the extent of Single Sign-On (SSO) policies. It appears that some government bodies had given super administrators extensive SSO privileges; stealing just one of these super admin credentials from an SSO effectively turns a segmented network into a flat network. Put another way, when the AI tools cracked these admin users’ passwords, they had almost a skeleton key to the entire network. Can’t AI developers stop their tools being used for this sort of purpose?AI tools do have some guardrails built in, but the evidence in this instance suggests they were bypassed, as the tools were told this was a penetration test. This underlines why organisations can’t rely on the AI tools to self-regulate through built-in safeguards; instead, it’s vital to retain network monitoring and intrusion detection systems, so that unwanted or unexpected actions on the network can be identified and addressed. How often should network segmentation policies be reviewed?Like all aspects of network security – including network configurations, firewall policies, router configurations, and access controls – segmentation should be reviewed regularly, or better still monitored continuously. Configuration drift, new devices, temporary permissions, and changing business requirements can gradually weaken segmentation controls over time: what was secure and segmented yesterday may not be today. Is network hardening still worth the effort against AI-enabled attacks?Absolutely. Network hardening reduces the number of weaknesses available for attackers to exploit. In this attack, the AI tools examined databases of known vulnerabilities to find new routes to probe. Network hardening to vendor recommended standards can shut these kinds of vulnerabilities down. How often should network hardening and patch status be reviewed?Like all aspects of network security, device status should be reviewed regularly against the latest vendor guidance and your own policies, or better still monitored continuously. New vulnerabilities are being discovered at a phenomenal rate – especially with the advent of AI tools. What was secure yesterday may not be today. That doesn’t mean you always need to panic-patch; vulnerabilities identified in a device may be irrelevant if you only use it on a wholly segmented internal network. But you do need to keep a close eye on what’s happening. How can Titania help organizations defend against AI-driven cyber threats?Titania's Nipper OmniSight helps organizations identify network security risks, visualize potential attack paths, validate segmentation policies, detect configuration drift, and prioritize remediation efforts so security teams can reduce exposure before attackers exploit it. |