AI Worms Explained: Adaptive Malware Threats

AI worms exploit LLMs and automation pipelines to spread without user interaction. Learn how these self-replicating threats work and how to defend against them with AI.

What Is an AI Worm?

An AI worm is a self-propagating type of malware designed to exploit large language models and their automation pipelines. This malware operates differently from traditional viruses: instead of dropping executable files, an AI worm injects self-replicating prompts that hijack an AI system's output, forcing every response, summary, or API call to carry the infection forward. The Morris II proof-of-concept, the first worm designed to target GenAI ecosystems, showed how a single poisoned email can make an assistant read, steal, and resend confidential messages across multiple AI platforms without any user interaction.

LLM APIs powering chatbots, retrieval-augmented-generation pipelines scouring internal knowledge bases, SaaS plugins handling email, and autonomous AI agents scheduling tasks all become ready-made propagation channels. Once inside, a threat hops between models through shared embedding stores or API calls, rewriting its own prompt to fit each context and evade signature defenses.

The zero-click nature of these attacks means you may never see the usual warning signs. Understanding how such threats move through interconnected systems becomes critical when your AI infrastructure expands.

Historical Roots: Morris Worm (1988) to Morris II (2024)

The original Morris Worm, released in 1988 by Cornell graduate student Robert Tappan Morris, became the first worm to gain significant attention for spreading across the early internet. Sometimes incorrectly referred to as the morris virus, it exploited vulnerabilities in Unix systems, particularly in sendmail and finger services, propagating through networks by guessing passwords and exploiting buffer overflows. While intended as a proof-of-concept to measure the internet's size, the worm's aggressive replication logic caused thousands of systems to crash, effectively taking down roughly 10% of internet-connected machines at the time.

Fast forward to 2024, when researchers at Cornell Tech and the Technion Institute named their GenAI proof-of-concept "Morris II" as a direct homage to that original worm. Rather than exploiting operating system vulnerabilities, Morris II targets the new infrastructure of interconnected AI agents. It demonstrated how adversarial prompts could hijack email assistants, forcing them to exfiltrate data and resend infected messages to new victims across ChatGPT, Gemini, and LLaVA platforms. The parallel is clear: both worms exploited the most transformative technology networks of their eras, exposing fundamental security assumptions that hadn't kept pace with innovation.

This evolution from exploiting network protocols to exploiting natural language processing shows how attack surfaces shift as technology advances. Where Morris targeted technical vulnerabilities in code, Morris II exploits the semantic understanding that makes LLMs useful, proving that the same self-replicating principles apply regardless of substrate.

Impact of AI Worms on Cybersecurity

AI worms can adapt to cyber security tactics in real-time and bypasses defenses put up by legacy signature-based solutions. They use machine learning to self-replicate malware and dynamically adapt their attack strategies. 

A classic example of this is the Morris II AI worm which was developed to exploit gen AI services. It ended up spreading and stealing data.

The key impacts of AI worms are:

• AI worms can do advanced evasions by using a mix of polymorphic and metamorphic techniques. They constantly change their code and behavior to go undetected by traditional antivirus solutions.
• They can rapidly scan networks, automatically exploit processes, and launch highly targeted attacks on systems. Their precision and level of accuracy can exceed human capabilities. 
• AI worms can craft highly personalized and convincing emails, deepfakes, and other deceptive content to trick victims and spread infections. They can also manipulate other gen AI tools used by organizations via adversarial self-replicating prompts to misdirect or trick systems.
• AI worms can expand attack surfaces and compromise critical infrastructure like power grids, financial networks, and internal processes. They can impact business continuity and affect supply chain operations negatively.

How Do AI Worms Work?

AI worms work via these key mechanisms:

• Adversarial self-replicating prompts - These are special prompts that compromise AI models and get them to generate malicious code by manipulating them. Common types are prompt poisoning, zero-click propagation, and prompt replication. These malicious prompts get stored in AI databases if apps use Retrieval Augmented Generation (RAG) technology.
• Evade and adapt - AI worms can analyze network activities, system resources, and security tools. They can identify patterns and learn tactics for evading detections. AI worms can try out different attack paths and continuously change their structures and behaviors to generate new signatures on the fly.
• Targeted social engineering - When it comes to targeted social engineering, AI worms can create realistic audio and video deepfakes to impersonate individuals. They can also manage and coordinate attacks across multiple communication channels at the same time. Automated spear phishing can also be done by them.
• Automated exploitation - Automated exploitation is another way AI worms work. They find and exploit vulnerabilities fast and deploy their newly found exploits. After a system is infected, AI worms can do automated payload delivery, exfiltrate data, deploy ransomware, and even spread spam.

How AI Worms Propagate

Before you can defend against an AI worm, you need to understand the four mechanics that let it bypass traditional controls and spread across your environment. These propagation methods distinguish AI worm malware from conventional threats that rely on file execution or network vulnerabilities.

• Adversarial self-replicating prompt injection forces an AI system to exfiltrate data and copy itself into every outgoing message. In the Cornell Tech proof-of-concept, a single crafted prompt made an email assistant steal inbox contents and repeat the cycle with any large language model that parsed the reply. This single vector transforms your AI assistant from a productivity tool into an automated data exfiltration engine that works around the clock.
• Model-to-model transmission happens through shared APIs, vector databases, and embedding stores. When multiple agents tap the same retrieval-augmented generation source, an injected payload in one collection point instantly becomes everyone's problem, turning your knowledge base into a distribution hub.
• External tool exploitation occurs when compromised LLMs call shell commands, SaaS plugins, or serverless functions. Each call inherits the adversarial instructions, giving it direct access to endpoints and cloud services where it can harvest secrets, spin up rogue workloads, or pivot laterally. A worm that controls your AI's tool access effectively owns every system that AI can touch, multiplying the attack surface exponentially.

• AI-generated spear-phishing completes the infection cycle. By mining public and internal data, AI-powered malware can craft highly personalized lures, deliver them at scale, and iterate wording until click-through rates soar. Every step is automated, so the threat spreads faster than security teams can triage alerts.

These mechanics exploit the always-on, agent-to-agent communication that powers modern workflows, giving an AI worm both the reach of a network threat and the stealth of a logic bomb.

How Social Engineering Boosts AI Worm Propagation

Phishing is already effective. Now imagine emails, voice messages, or video deepfakes generated by an LLM that has studied your writing style, calendar, and recent tickets. The research on Morris II showed how an infected agent can analyze a target's preferences, adjust tone on the fly, and embed a fresh self-replicating prompt in each reply.

Because the content feels human and context-aware, filters based on static signatures let it through, and recipients instinctively trust it. The threat then rides those responses back into corporate chatbots, ticketing assistants, or CRM automations, widening the blast radius without a single malicious attachment.

You face an adversary that drafts perfect bait in seconds, delivers it at machine speed, and pivots the moment you tweak a rule set: social engineering at scale, powered by your own AI stack. In order to defend against this new threat, it's important to understand how AI worms differ from traditional worms, beyond propagation.

Key Characteristics: AI Worms vs. Traditional Worms

You've likely wrestled with classic network threats before, but AI-powered variants raise the stakes by evolving in real time. Emerging adversaries could soon merge the capabilities of self-replicating malware with prompt engineering and generative AI's automation, giving them an adaptive edge.

Attribute	Traditional threats	AI-powered variants
Payload evolution	Fixed code; updates require a new build	Learns from each interaction and rewrites its own prompts or code on the fly
Propagation vector	Exploits known vulnerabilities in operating systems or network protocols	Manipulates natural-language APIs and agent-to-agent communication
Detection surface	File signatures, network traffic patterns, registry changes	Behavioral anomalies in API usage, token consumption, inter-agent chatter
Speed of spread	Minutes to hours as it scans networks for vulnerable hosts	Seconds as it rides automated workflows and API calls
Containment strategy	Patch systems, isolate infected machines, block malicious IPs	Quarantine models, rotate API keys, retrain or roll back agents, audit all privileges

This table highlights why familiar patch-and-scan playbooks no longer suffice. When a threat can rewrite itself in response to your defenses, you need behavioral AI that watches for deviations and responds autonomously.

Prevention Strategies

Stopping an AI worm before it enters your environment requires proactive security measures that address both infrastructure and human factors. Prevention strategies create multiple barriers that attackers must breach, dramatically reducing your attack surface.

1. Implement strict API authentication and rate limiting

Enforce multi-factor authentication for all API access and set aggressive rate limits on model queries. Restrict API keys to specific IP ranges and services. Monitor token consumption patterns to flag accounts that suddenly spike in usage, a common indicator of automated exploitation. These controls force attackers to work slowly and visibly, giving your security team time to intervene.

2. Maintain segmented AI environments

Isolate development, staging, and production AI systems with strict network boundaries. Never allow direct communication between customer-facing chatbots and internal knowledge repositories. Use separate embedding stores for different risk levels. Segmentation means a compromised public demo can't reach your proprietary training data or production workflows.

3. Conduct regular security awareness training

Train employees to recognize AI-generated phishing attempts, especially those that mimic internal communication styles or reference recent work. Run simulated attacks using AI-generated content to test response protocols. Update training quarterly as threat techniques evolve. Human vigilance remains your first line of defense against social engineering attacks that bypass technical controls.

4. Deploy input validation and content filtering

Sanitize all prompts before they reach your LLM by stripping special characters, system commands, and embedded instructions. Validate outputs against allow-lists of acceptable actions before executing any tool calls. Reject prompts that attempt to override system instructions or access restricted data. These filters catch malicious payloads at ingestion, preventing infection at the source.

5. Establish zero-trust architecture for AI systems

Require explicit authorization for every AI-to-AI interaction and tool invocation. Never grant broad permissions based on initial authentication. Log every API call with full context for audit trails. Automatically revoke access after sessions expire. Zero-trust principles ensure that even if an attacker compromises one component, they can't move laterally without triggering multiple authorization failures.

Prevention works best when layered with the detection strategies outlined below. While these measures significantly reduce risk, no single approach provides complete protection against adaptive AI threats.

Defense Strategies

Stopping an AI worm requires a layered approach that addresses both the adversarial prompts and the automated workflows they exploit.

1. Input sanitization and output validation

Strip adversarial instructions from prompts before they reach your LLM. Validate every response against a policy that blocks embedded commands, suspicious API calls, or attempts to exfiltrate data. This creates a checkpoint that catches malicious payloads before they propagate.

2. Model isolation and API segmentation

Segment your AI agents so a compromised chatbot can't reach your knowledge base or cloud services. Use least-privilege access controls for every API key and service account. If one model falls, isolation limits the blast radius.

3. Behavioral anomaly monitoring

Watch for unusual patterns: an agent requesting an API scope it never needed, a spike in token usage, or a sudden burst of outbound emails. Behavioral AI flags these deviations long before human analysts would notice them.

4. Autonomous EDR/XDR with behavioral AI

AI-powered threats rewrite themselves on the fly, making signature-only tools useless. Platforms such as SentinelOne's Singularity use static and behavioral AI to flag unusual inter-agent chatter or sudden credential harvesting.

5. Quarterly table-top exercises and runbook refresh

These threats propagate in seconds; your response playbook can't take hours. Simulate infections, rehearse containment steps, and update runbooks every quarter.

Behavioral AI is the common thread: it watches for deviations that reveal automated spread long before human analysts would notice. Singularity's AI SIEM extends that visibility across endpoints, cloud workloads, and identities in one console.

Incident-Response Workflow

When facing an active AI threat, speed is everything. Here's your essential response workflow:

• Identify and isolate the infected model or plugin
• Contain affected endpoints with network quarantine within seconds
• Roll back or retrain the compromised model from a clean snapshot
• Rotate all secrets, API keys, and OAuth tokens the agent could touch
• Audit logs for lateral movement and revoke any suspicious privilege changes

An AI-powered threat can leap between agents in the time it takes to read an alert. A rehearsed workflow turns panic into procedure and buys you the minutes needed to cut the infection chain. Understanding these defenses becomes even more critical when you consider how many organizations still hold outdated assumptions about AI security.

Common Mistakes and Misconceptions

Even seasoned defenders still cling to a few myths about AI threats that can leave you dangerously exposed. Let's clear them up.

"AI-powered threats are still sci-fi."

While computer virus news today still focuses heavily on ransomware and traditional malware, the Morris II proof-of-concept already stole emails, spammed new victims, and re-infected ChatGPT, Gemini, and LLaVA during live research demos as well as zero-click propagation in real time. Those demonstrations make the threat tangible today, not tomorrow.

If your security strategy assumes AI threats remain hypothetical, you're leaving your environment unmonitored for an entire class of attacks. Start by inventorying every AI system in your infrastructure, from internal chatbots to third-party APIs. Implement behavioral monitoring on those systems immediately and establish baseline usage patterns. Test your incident response team's ability to quarantine compromised models using tabletop exercises focused specifically on AI attack scenarios.

"Legacy AV is enough."

Traditional antivirus looks for static file signatures; AI threats hide in natural-language prompts and adapt on the fly, a behavior that slips past signature engines and even polymorphic-malware heuristics. An AI virus can rewrite itself between infections, making traditional pattern matching ineffective.

Relying solely on signature-based detection means you won't see an AI worm until it has already spread across your agent infrastructure. Upgrade to behavioral AI-powered XDR that monitors for anomalous API calls, unusual token consumption, and suspicious inter-agent communication patterns. Audit your current security stack to identify gaps in AI system visibility, then implement monitoring that captures prompt injection attempts and adversarial output patterns before they execute malicious actions.

"Only AI vendors are at risk."

Any organization that embeds large language models creates new entry points. Think RAG knowledge bases, SaaS plugins, or internal chatbots. An infected wiki page or API request can propagate the threat across your entire workflow stack.

The misconception that AI threats only affect AI companies leaves most organizations blind to their actual exposure. If you use ChatGPT integrations, Slack bots with LLM capabilities, or automated email assistants, you're already running AI infrastructure. Map every instance where your organization uses generative AI, including shadow IT deployments that security teams might not know about. Implement access controls and monitoring on these systems exactly as you would for any other critical infrastructure component. Don't wait until a breach forces you to discover how deeply AI has integrated into your operations.

Dispelling these myths helps you see why familiar playbooks no longer suffice.

Examples of AI Worms

Morris II was the first gen AI worm to be created in 2024. It showed the security risks of AI systems and exploited vulnerabilities in Retrieval Augmented Generation (RAG) components. Morris II had spread misinformation, exfiltrated data from gen AI apps, and distributed malware to other AI agents.

Cybersecurity researchers had also managed to get Lena, Lenovo's AI chatbot, to cough up sensitive information and execute malicious code. Lena's outputs had persisted in the conversation history and it also helped generate malicious HTML and payloads once it got infected.

Then, we have the case of AI-enabled malware which are not true AI worms but come close enough. Stuxnet, WannaCry, and other strains of AI-generated malware don't need human intervention. They can create polymorphic malware, evade detection, and use AI to autonomously scan vulnerable targets and spread rapidly across networks.

Stop AI Worms with SentinelOne

You need defenses that think and act as fast as the malware they fight. SentinelOne's Singularity™ Platform brings autonomous prevention, investigation, and remediation together in one console, using behavioral AI to spot the rapid lateral movement, zero-click propagation, and prompt-driven anomalies that signal an AI worm. When a suspicious chain appears, the platform isolates the endpoint, rolls back malicious changes, and blocks further propagation in real time before human analysts even open a ticket.

Purple AI is the world’s most advanced gen AI cybersecurity analyst; it enables both novice and experienced responders to investigate threats faster using natural language queries instead of complex query languages. It conducts autonomous threat hunting, translates your questions into power queries, and suggests next investigative steps based on contextual threat intelligence. Investigation notebooks let teams collaborate on complex cases, while auto-summaries accelerate response times. SentinelOne also delivers 88% fewer alerts compared to legacy systems, reducing the number of false alerts while maintaining complete visibility across endpoints, cloud workloads, and identities. 

Prompt Security also helps you defend against emerging AI threats including prompt injection, model poisoning, denial-of-wallet and denial-of-service attacks, while blocking unauthorized or shadow AI usage. Its content moderation and anonymization controls prevent sensitive data leaks when using AI models and tools, and it stops unauthorized agentic AI actions to keep users safe from harmful LLM outputs. 

Prompt Security powered by SentinelOne can apply safeguards to AI agents to ensure safe automation at scale. You also have SentinelOne's AI Security Posture Management that can help you discover AI pipelines and models. It can configure checks on AI services. You can also leverage Verified Exploit Paths™ for AI services.

Conclusion

AI worms exploit LLM vulnerabilities through adversarial prompts, spreading via agent-to-agent communication without user interaction. These threats adapt in real time, bypassing signature-based defenses. Stop them with behavioral AI that monitors for anomalies, input sanitization, API segmentation, and autonomous XDR. Legacy tools can't keep pace with self-rewriting malware.

The zero-click nature of these attacks means infections spread in seconds rather than hours, moving through RAG pipelines, SaaS plugins, and shared embedding stores before security teams even receive alerts. Prevention requires strict API authentication, segmented AI environments, and regular security training to counter AI-generated social engineering. While AI worms remain largely in research environments, organizations should prepare incident response playbooks and conduct quarterly tabletop exercises now rather than wait for the first production breach.

AI Worms FAQs

What are AI Worms?

AI worms are self-propagating malware programs specifically designed to exploit large language models and AI automation systems. Unlike traditional worms that target operating system vulnerabilities, AI worms inject malicious prompts that hijack AI outputs, forcing infected systems to spread the attack through every response, API call, or automated message. 

These threats move through agent-to-agent communication channels, using natural language processing vulnerabilities to bypass conventional security tools.

How Security Teams Can Detect and Mitigate AI Worms?

Security teams can use AI-powered solutions like SentinelOne to find and mitigate AI worms. They should also update their systems, apply network segmentation, and use access controls. Regularly patching vulnerabilities and continuously training their employees to stay aware and prepared can limit and contain how AI worms propagate.

How do AI Worms differ from Traditional Malware?

Traditional malware relies on file execution, network exploits, and operating system vulnerabilities that signature-based antivirus can detect. AI worms operate through natural language prompts that appear as legitimate queries, making them invisible to conventional security tools. 

While traditional threats require software updates to evolve, AI worms can rewrite their own instructions in real time, adapting to defenses and changing tactics between infections. They spread through API calls and agent-to-agent communication rather than file systems, fundamentally changing how attacks propagate.

What is the Biggest Threat of AI?

The biggest threat of AI in cybersecurity is self-replicating malware that weaponizes adversarial prompts to exploit LLM automation without human interaction. AI worms can adapt their attack code in real time, bypass signature-based detection by hiding in natural language, and spread through legitimate API channels that security tools trust by default. 

Combined with AI-generated social engineering that crafts personalized phishing at scale, these threats move faster than traditional response playbooks allow. The automation multiplies both attack speed and sophistication beyond what defenders currently prepare for.

Are there known AI worm attacks documented? Are AI threats in the wild today?

Not yet. They've only appeared in controlled demonstrations like the Morris II proof-of-concept that hijacked email assistants and LLMs across platforms. The threat remains theoretical but increasingly realistic as more organizations integrate generative AI into their workflows. Security teams should prepare defenses now rather than wait for the first documented incident in production environments.

How hard are AI Threats to Detect?

Extremely difficult. Their payloads hide inside natural-language prompts, completely bypassing signature-based detection. Traditional security tools scan for malicious files or network traffic patterns, but AI worms operate through legitimate API calls and model interactions. 

You need behavioral analysis that flags unusual token consumption, unexpected API scopes, or sudden spikes in agent-to-agent communication to catch the anomalies before they spread.

What are the signs my computer has a worm?

AI worms are particularly difficult to spot because they don't create traditional infection markers like suspicious files or registry changes. Watch for unusual AI system behavior: unexpected spikes in API calls or token consumption, agents requesting permissions they never needed before, sudden increases in outbound messages from chatbots or email assistants, or your AI tools producing outputs that don't match their normal patterns. 

Traditional worm symptoms like system slowdowns or network congestion may not appear since AI worms operate through legitimate automation channels. Behavioral monitoring tools that track API usage patterns provide the best detection capability.

Should you ban ChatGPT to Prevent Infection?

Blanket bans create bigger problems than they solve. Shadow IT emerges as employees use unsanctioned tools anyway, removing your visibility into AI usage. Instead, sanitize inputs and outputs, lock down model permissions, and monitor AI activity for prompt injection attempts. Smart controls that allow AI use within guardrails beat wholesale prohibition while maintaining security and productivity

What first steps should a mid-market company take to avoid AI worms?

Segment your networks to limit lateral movement if an AI agent becomes compromised. Deploy multi-factor authentication everywhere to protect against credential harvesting. Regularly conduct red-team exercises that specifically assess your AI workflows, testing how adversarial prompts might propagate through your systems. 

These foundational moves contain outbreaks before they cascade through your environment and buy time for your security team to respond.

How do AI threats compare to traditional computer threats in threat level?

They're worse. AI-powered variants learn and adapt in real time, evading traditional defenses by rewriting themselves and exploiting new attack vectors beyond operating system vulnerabilities. Traditional antivirus and endpoint protection were built for static malware that follows predictable patterns. 

AI worms analyze your defenses, modify their payloads to slip past controls, and spread through legitimate automation channels that security tools trust by default.

Which industries are most at risk?

Any sector running heavy automation faces elevated exposure. Finance, healthcare, SaaS platforms, and critical infrastructure top the list because interconnected AI agents amplify threat reach exponentially. Financial institutions process millions of automated transactions daily, healthcare systems integrate AI for diagnostics and patient management, and SaaS platforms power enterprise workflows. Each integration point becomes a potential propagation vector for AI worms.