We’re reaching the end of 2025, and looking ahead to 2026, most experts are discussing the latest threats that will shape the year ahead. This year, we’re seeing a new, but not unexpected, shift to autonomous threats driven by state-sponsored actors and AI.

With that in mind, a new generation of threats, broadly known as autonomous malware, is beginning to reshape how organizations think about cyber risk, detection, and response. These threats don’t behave like the malware that defenders have spent decades learning to identify, and that’s got experts preparing for the new threat landscape.

This article explains what autonomous malware is, why it matters now, and what experts should watch as these threats evolve.

What Is Autonomous Malware?

Traditional malware follows a predictable pattern: infect, communicate with the attacker, move laterally, exfiltrate, or detonate a payload. Anti-malware software has relied on the fact that while this malicious software can change its behavior, it can’t change its structure.

Instead of treating the infected environment as a fixed script to step through, autonomous malware treats it as a dynamic environment. It can test multiple escalation paths and choose the one with the lowest likelihood of detection. Some strains leverage reinforcement-learning models for decision-making. Others use AI-generated code mutators to evade detection in real time. And attackers can deploy these tools in a decentralized manner, allowing malware agents to operate semi-independently with high-level goals rather than step-by-step instructions.

Examples of Autonomous Malware

Ironically, the idea of autonomous malware isn’t new. Famous worms and viruses, from Stuxnet to WannaCry, exhibited early signs of autonomous behavior. Still, it was in the past few years that we began to see massive leaps in this technology.

Peer-to-Peer Botnets are a form of autonomous malware that communicate with a network of peers to influence their behavior. To work, however, these entities require several layers of self-directed behavior. Some good examples are the Hajime and Mirai botnets.
NoAuth Worm is one of the first cloud-native, autonomous malware packages in the wild, targeting poorly configured cloud admin consoles and deploying without a human operator. The infamous WannaCry worm is an example of this malware.
QakBot was a dynamic malware that could monitor its environment as needed, checking the OS and deploying different tools and lateral propagation methods based on that information.

Now, with the rise of LLMs, several proof-of-concept malware packages have been developed alongside platforms like ChatGPT. These include the Morris II worm, the BlackMamba malware, and Wolverine Agent.

What Makes Malware Autonomous?

While there have been several examples of malware that’s pushing the line of what is “autonomous,” true autonomous malware will share some primary characteristics. In practical terms, autonomous malware is defined by three key capabilities.

Adaptive Decision-Making

Autonomous malware chooses its next action based on context rather than a preset algorithm. If it detects a strong EDR presence, for example, it may select a stealthier persistence mechanism. If it senses a sandbox, it may delay execution or adopt a different activity until it sees an opening into production systems.

This adaptiveness mirrors how AI agents operate. The best action depends on the environment, and the malware continuously evaluates it.

C2-Independent Operation

Many organizations rely heavily on detecting or disrupting malware’s command-and-control channels. But since autonomous malware isn’t as reliant on C2 (if at all), security operations built around this tactic aren’t going to be as effective.

Some even use opportunistic peer-to-peer fallback channels, coordinating with other infected hosts without needing a central server. Others share small data packets when hosts come into network proximity, allowing infections to act as a swarm.

Self-Mutating or Self-Optimizing Behavior

Self-writing programs have been a massive holy grail for hackers. Malware that can rewrite itself can effectively mutate to any form it needs, taking away one of the more important approaches anti-malware software relies on.

Autonomous malware, however, can shift behavioral patterns to avoid detection thresholds. It can learn from failed attempts and adjust future strategies accordingly. This means defenders can no longer rely on signatures or previous behavioral patterns.

Why is Autonomous Malware Evolving Now?

Several converging forces are accelerating the shift toward autonomy:

Generative AI lowered the skill barrier. Attackers no longer need deep technical expertise to build sophisticated payloads. They only need baseline capability and the willingness to experiment.
Agentic AI frameworks have matured. With open-source models capable of planning, reasoning, and multi-step execution, it became easier for attackers to embed autonomous logic into malware.
Cybercrime groups are more decentralized. Ransomware-as-a-service operators want tools that remain functional even when communication channels are disrupted or when affiliate operators go offline.
EDR and XDR advancements forced evolution. As defensive tools became faster and behavioral analytics became more sensitive, attackers shifted toward unpredictable decision-making to avoid triggering deterministic rules.

By 2026, these factors will have nudged attackers toward building malware that behaves less like a script and more like a strategist.

What Autonomous Malware Looks Like in the Wild

Fully realized autonomous malware will exhibit common characteristics that drive dynamic attack patterns suited for APTs.

Consider what happens when autonomous malware lands on a workstation:

It observes. What processes are running? What privileges are available? What security controls are present? This reconnaissance isn’t static; it’s continuous.
It propagates. If privilege escalation looks risky, it may attempt credential capture instead. If lateral movement is too obvious, wait until user activity masks the anomalies.
It acts incrementally. Instead of a noisy burst of activity, it progresses in smaller, intention-driven steps, dynamically adjusting its approach.
It persists. If a piece of the malware is removed, it can restore itself from redundant components or pivot to a different foothold.
It continues without the need for direct command. Even in a fully isolated network environment, autonomous malware can conduct meaningful operations, exfiltrating data when connections return or spreading through low-risk internal vectors.

How Can You Protect Your IT from Autonomous Malware?

Autonomous malware will demand a lot from security experts and business leaders over the next few years. There really isn’t going to be a way to avoid overhauling security priorities to focus on privileges, zero trust, and automated compliance and security response.

The following areas are becoming essential focus points beyond requirements in their compliance checklist.

Shift to Zero Trust by Default

Autonomous malware thrives in flat, trusting networks. Consequently, zero-trust systems can prevent malware from gaining a foothold. Segmentation limits its mobility and reduces the number of “decision pathways” it can explore. Even partial segmentation, implemented consistently, can significantly blunt autonomous propagation.

Harden Identity Everywhere

In 2026, organizations should prioritize phishing-resistant MFA, the principle of least privilege, and continual control and review of privileges, service accounts, and user/agent identities.

Use Behavioral and Sequence-Based Analytics

Static signatures and simple behavioral flags won’t reliably catch autonomous malware. Instead, organizations need analytics that detect “impossible sequences,” privilege escalation chains, or lateral movement patterns that don’t match legitimate workflows.

Automate Incident Response

We’re quickly approaching the point where we can no longer rely on human-scale response times. Modern malware will require autonomous responses to stay ahead of threats. Following that, automated containment (isolating hosts, disabling compromised tokens, or rolling credential resets) must become normal. Many autonomous threats execute faster than SOC teams can triage alerts.

Align Compliance and Modern Security with Lazarus Alliance

As we move into 2026, autonomous malware is sending a clear message: defenders are no longer fighting static code, but adaptive agents capable of improvisation. That shift requires new defensive strategies, new tooling, and a deeper organizational awareness of evolving risks.

To learn more about how Lazarus Alliance can help, contact us.

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