AI Threat Detection for Botnets, IoT, and Supply Chain

Most security teams still treat “this week’s threats” like a list of unrelated fires. That’s exactly what attackers want.

This week’s headlines connect in a pretty uncomfortable way: botnets are evolving beyond noisy DDoS, malware is getting better at hiding on Linux and Windows, software supply chains are leaking secrets at scale, and attackers are actively abusing AI platforms to make scams look trustworthy. If you’re trying to defend an enterprise (or a public sector network) with manual triage and rule-by-rule detection, you’re showing up to a drone fight with a flashlight.

This post is part of our AI in Cybersecurity series, and I’m going to take a stance: AI-driven threat detection isn’t optional anymore. Not because it’s trendy—because the speed, volume, and shape-shifting nature of modern attacks now exceeds what humans and static tools can reliably keep up with.

Mirai isn’t “old news” — it’s a template for modern botnets

Answer first: Mirai’s persistence matters because attackers keep upgrading it, and AI-based detection is one of the few practical ways to catch these variants when signatures fail.

A new Mirai variant (Broadside) targeting maritime logistics illustrates a broader pattern: IoT threats aren’t just about knocking services offline. Broadside reportedly adds stealth features and tries to maintain exclusive control of compromised devices by killing competing malware/processes. That’s not spray-and-pray botnet behavior—it’s territory management.

Here’s what should make defenders uneasy:

• Custom C2 protocols reduce the value of traditional network signatures.
• Polymorphic payloads reduce the value of file-hash detection.
• Event-driven stealth (kernel-level behaviors) reduces the value of noisy “watch for weird files” approaches.
• Credential harvesting turns a “dumb device compromise” into a stepping-stone toward core systems.

What AI can do here that rules can’t

Most companies try to write detections for “Mirai traffic.” That’s brittle. A more durable approach is to detect behaviors that botnets must exhibit to operate at scale.

AI helps by learning baselines and flagging deviations across thousands of devices where human analysts can’t memorize “normal”:

• Device behavior baselining: A maritime DVR shouldn’t suddenly enumerate credential files or spawn unusual processes.
• C2 pattern discovery: Even with custom protocols, botnets often create repeatable rhythms (beacon cadence, burst patterns, retry logic).
• Kill-competitor patterns: Terminating processes by path pattern is a weirdly specific behavior that stands out when you model process trees and termination events.

If you’re defending IoT-heavy environments, the winning move is anomaly detection + automated containment, not a longer list of signatures.

Zero-days and mass exploitation are now “calendar events”

Answer first: When exploitation scales globally within days, AI-driven triage and patch prioritization become the difference between a scare and a breach.

Look at the exploitation pattern around a React flaw being used to deliver botnet payloads across smart home devices, routers, NAS systems, and more. Researchers observed hundreds of unique IPs across ~80 countries probing for the same weakness.

That’s the reality of 2025: once an exploit path is public, it gets automated and distributed immediately. Your exposure window isn’t “until next quarter’s patch cycle.” It’s until the next wave of opportunistic scans hits your IP range.

How AI changes vulnerability response (when used correctly)

AI won’t magically patch your fleet. But it can shrink time-to-action by turning scattered signals into prioritized work.

Practical ways teams are using AI in vulnerability management:

1. Exploit likelihood scoring with live telemetry
   • Combine asset criticality, internet exposure, observed scanning, and exploit chatter.
2. Patch prioritization that respects operations
   • Recommend a patch order that reduces risk fastest without taking down production.
3. Detection engineering acceleration
   • Generate and test detection hypotheses (process + network + identity) faster than a human writing from scratch.

Here’s the point: speed is now a control. If you can’t react quickly, you’re not “behind”—you’re predictable.

Linux and Windows stealth: the quiet part is getting louder

Answer first: Rootkits and low-noise backdoors are advancing, and AI helps by correlating weak signals across endpoints, kernel events, and network behavior.

Two developments matter here:

• A Linux backdoor using UDP port 53 for command-and-control—a choice that blends into DNS-shaped noise.
• A newer syscall hooking approach (post Linux kernel changes) that makes traditional rootkit detection harder.

On the Windows side, analysis of ValleyRAT’s ecosystem shows something many defenders still underestimate: crimeware developers are borrowing techniques that look like advanced intrusion tradecraft, including kernel-mode components and attempts to disable security drivers.

What “AI detection” should mean for endpoint stealth

A lot of vendors sell “AI” and still rely on old patterns. What actually works is multi-signal correlation:

• Process lineage + memory behaviors: suspicious injection chains, abnormal APC usage, LOLBin misuse.
• Kernel-to-user discrepancies: what the OS reports vs. what telemetry suggests is actually running.
• DNS-shaped C2 behaviors: unusual UDP 53 usage, odd query entropy, persistence patterns.

AI is valuable because stealthy threats rarely trip one obvious alarm. They trip five small ones that only look meaningful when you connect them.

Supply chain reality check: secrets are spilling everywhere

Answer first: Secret leakage is now a leading indicator of compromise, and AI helps you find exposed credentials before attackers do.

A recent analysis found 10,000+ container images exposing credentials, and 42% of those images contained five or more secrets. Even more telling: LLM/API keys were among the most commonly leaked, signaling that AI adoption is outpacing security hygiene.

This isn’t just “oops, someone committed a token.” It’s an operational pattern:

• Engineers ship containers quickly.
• CI/CD pipelines cache credentials.
• Teams treat API keys as “less serious” than passwords.
• Attackers search public registries and repos like it’s their day job.

The AI-enabled approach to secret sprawl

You don’t fix this with a stern email. You fix it with automation that keeps working on Friday night.

A pragmatic program looks like this:

• Continuous secret scanning across repos, images, and build logs (not quarterly).
• Context-aware triage: AI can reduce noise by classifying which strings are real secrets and which are test data.
• Automated rotation workflows: when a secret is found, rotate and revoke quickly, then open a ticket with exact file paths and build provenance.
• Policy-as-code guardrails: block builds that introduce high-confidence secrets.

If your org is deploying GenAI features, treat your model keys like production admin credentials—because attackers do.

Prompt injection and “AI trust” are becoming attacker infrastructure

Answer first: Prompt injection isn’t a bug you’ll patch away; you have to design GenAI systems so the model can’t take dangerous actions.

The U.K. NCSC’s position that prompt injection “will never be properly mitigated” is the most useful framing I’ve seen: stop chasing perfect filters. Constrain what the system can do.

At the same time, attackers are using shared AI chats and SEO manipulation to distribute stealer malware via convincing “helpful” troubleshooting steps—often instructing users to paste terminal commands.

This is social engineering with an upgrade: people trust AI-shaped answers, especially when they appear in search results and look like a familiar chat interface.

Guardrails that actually reduce GenAI risk

If you’re deploying GenAI internally (or exposing it to customers), prioritize controls that limit blast radius:

• Tool permissions: the model shouldn’t have access to sensitive systems by default.
• Action gating: require human approval for high-risk actions (payments, account changes, code execution).
• Retrieval boundaries: restrict what data the model can fetch; log every retrieval.
• Prompt injection testing: red-team the system continuously, not as a one-time pre-launch task.

And for user safety, implement training that’s blunt and specific:

If an “AI guide” tells you to paste a command into a terminal, treat it like a random executable.

A practical AI-enabled detection playbook (90 days)

Answer first: The fastest path to better outcomes is to focus AI on triage, correlation, and containment—then measure time saved and incidents prevented.

If you’re trying to turn “we should use AI” into an operational plan, here’s what works in the real world.

Days 0–30: Get your signals in order

• Centralize endpoint, identity, DNS, proxy, and cloud logs.
• Normalize asset identity (hostnames, device IDs, owners, business criticality).
• Define what “containment” means for IoT vs. laptops vs. servers.

Days 31–60: Use AI where it pays back immediately

• Anomaly detection for IoT behavior and DNS/C2 patterns.
• AI-assisted alert clustering to cut duplicate triage.
• Exploit-aware patch prioritization tied to real exposure.

Days 61–90: Automate response for repeatable threats

• Auto-isolate devices exhibiting botnet-like behavior.
• Auto-disable accounts showing stealer-like session hijacks.
• Auto-rotate secrets detected in build artifacts.

The metric I care about most isn’t “number of AI models deployed.” It’s median time to detect + contain.

What to do next (and what to stop doing)

AI in cybersecurity works when it’s aimed at the right problem: detecting patterns humans can’t see quickly enough, and triggering actions humans can’t execute fast enough.

Stop assuming botnets are just DDoS noise. Stop treating container images like opaque blobs. Stop expecting prompt injection to be “solved” with better prompts. The threats in this week’s bulletin are different chapters of the same story: attackers are optimizing for scale, stealth, and trust.

If you’re building your 2026 security roadmap right now, the question that matters is simple: Where are you still relying on manual attention for problems that attackers have already automated?