A previously undocumented advanced persistent threat (APT) group has been quietly breaching government agencies and electric power providers across at least three countries. Dubbed Armored Likho APT by researchers at Kaspersky, the actor blends financial theft with espionage, using a flexible toolkit that evolves on the fly.

Armored Likho’s operations stretch from Russia to Brazil and Kazakhstan. The group targets both individuals, for monetary gain, and larger organizations, for intelligence. Its arsenal includes modular remote access trojans (RATs) and information stealers, most notably a Python-based payload Kaspersky tracks as BusySnake Stealer.

This isn’t a one-trick pony. The malware stack allows the attackers to maintain stealthy control over compromised machines, siphon credentials, and deploy additional modules tailored to each victim. Kaspersky’s analysis, published in early July 2026, details how the group achieves this with surprising agility.

How Armored Likho Gets In

The primary entry vector is spear-phishing. Emails carry archives containing executable files or LNK shortcuts. When a target opens the attachment, a decoy document appears on screen. Behind the scenes, the real payload installs silently.

One loader, injected directly into memory, was observed fetching additional archives from public GitHub repositories. Those repos held early development builds and test samples. Another method uses LNK files that pull down a full Python 3.12 interpreter and a malicious archive while displaying a fake document to the user.

BusySnake Stealer: The Core Weapon

The centerpiece of Armored Likho’s toolset is BusySnake Stealer, a Python-based infostealer packed with evasion tricks. It decrypts bytecode only when a specific function is called, then re-encrypts it immediately after execution. The process runs without a console window, keeping it out of sight.

BusySnake relies on multiple handlers for different tasks. These include clipboard theft, file enumeration, extraction of 64-character hexadecimal keys, document exfiltration, screenshot capture and archiving, persistence checks, and general command execution.

Commands from the C&C server can trigger a wide range of actions: capturing screenshots, exfiltrating logged keystrokes, decrypting stored passwords from Chromium-based and Firefox browsers, stealing cookies, scraping the machine for OTP keys, locating cryptocurrency wallets, harvesting Telegram sessions and credentials, establishing a reverse SSH tunnel, or even restarting RustDesk to capture user credentials.

From Go2Tunnel to Full Control

Earlier campaigns relied on a separate tool called Go2Tunnel to set up reverse SSH tunnels. Armored Likho has since folded that capability directly into BusySnake Stealer. The infostealer now provides persistent remote access and interactive control over the victim’s system without needing a secondary utility.

Overlap With Eagle Werewolf

Kaspersky notes that Armored Likho’s operations show overlap with a known hacking group called Eagle Werewolf. That group previously used a RAT named AquilaRAT, which shares structural similarities and persistence mechanisms with BusySnake Stealer. The connection suggests the same developers may be behind both toolkits.

The shift toward modular, Python-based malware is a trend worth watching. It gives attackers the flexibility to swap components quickly and evade signature-based detection. For defenders in the government and electric power sectors, this means staying ahead of an adversary that adapts its code as easily as it changes its targets.

As Armored Likho continues to refine its methods, organizations should prioritize email security, monitor for unusual GitHub repository access, and keep an eye out for the telltale signs of BusySnake Stealer deployment.

A New Phishing Wave Hits Marketing Professionals

A sophisticated phishing campaign is making the rounds, and it has a very specific target: marketing professionals. The hook? Fake job offers from some of the world’s biggest brands. The goal? Stealing their Google accounts.

Security researchers have identified a sharp uptick in these attacks, which rely on a mix of social engineering and technical trickery to bypass even cautious users. If you work in marketing — or know someone who does — this is worth paying attention to.

How the Scam Unfolds

The attack starts with an email that looks legitimate. It appears to come from a recruiter at a well-known company — think Google, Amazon, or Meta. The subject line often mentions a job opening, a request for an interview, or an invitation to apply for a role that matches the target’s profile.

Curiosity gets the better of most victims. They click a link that supposedly leads to a job description or an application portal. But that’s where the trouble begins.

Nested Redirects: The Core Trick

Instead of taking the user straight to a phishing page, the link goes through a series of intermediate redirects. This technique, known as nested redirecting, makes it much harder for security scanners and email filters to detect the malicious destination. Each hop looks harmless on its own. By the time the user lands on a fake Google login page, the trail has gone cold.

The fake page is convincing. It mimics Google’s real sign-in interface, right down to the logo and the layout. The victim enters their email and password — and that’s it. The credentials are sent straight to the attackers.

Why Marketers Are Prime Targets

This isn’t random. Attackers are deliberately going after marketing professionals. Why? Because a marketing person’s Google account often holds the keys to the kingdom: Google Ads accounts, Google Analytics profiles, access to the company’s YouTube channel, and shared Google Drive documents filled with strategy and data.

One compromised account can give a criminal access to ad budgets, customer data, and internal communications. It’s a high-value target wrapped in a low-suspicion package — a job offer.

“Marketing teams are used to receiving unsolicited emails from recruiters. It’s part of the job,” says one cybersecurity analyst tracking the campaign. “That familiarity is exactly what the scammers are exploiting.”

Red Flags to Watch For

So how do you tell a real job pitch from a phishing attempt? Look for these signs.

• Check the sender’s domain carefully. A recruiter from a big brand will use a corporate email address — not a Gmail address or a lookalike domain with a typo (e.g., amaz0n-careers.com).
• Hover over links before clicking. See where they actually lead. If the URL looks strange or doesn’t match the company’s real website, don’t click.
• Beware of urgency. Emails that pressure you to act quickly — “Apply within 24 hours!” — are a classic phishing tactic.
• Never enter your Google credentials on a page you reached from an email link. Go directly to the company’s career page instead.

What to Do If You’ve Been Targeted

If you suspect you’ve clicked a malicious link, act fast. Change your Google password immediately. Enable two-factor authentication if you haven’t already — it’s the single best defense against credential theft.

Also check your Google Account’s recent activity and review any devices or apps that have access. Revoke anything you don’t recognize. If you use the same password on other sites, change those too. For more on staying safe, read our guide on how to spot phishing emails.

Report the phishing email to your company’s IT or security team. They can block the sender and warn others. You can also forward it to Google’s phishing reporting address: phishing@google.com.

The Bigger Picture

This campaign is a reminder that phishing isn’t just about fake bank alerts or Nigerian prince emails anymore. Attackers are getting smarter. They’re doing their homework, targeting specific roles, and using techniques like nested redirects to stay under the radar.

For marketing professionals, the lesson is simple: treat every unsolicited job offer with a healthy dose of skepticism. The promise of a dream role at a big brand could be the bait that costs you your account — and a lot more.

The Old Problem Was Hard Enough

For the better part of a decade, “software supply chain security” meant one thing: tracking what’s inside your code. Which open-source packages. Which versions. Which transitive dependencies three layers deep that nobody consciously chose.

SolarWinds, Log4Shell, and XZ Utils all taught the same bitter lesson. The risk lives less in the code a team writes itself, and more in the millions of lines they pull in from strangers on the internet. Locking down that pipeline became an industry obsession.

Then AI joined the build. And the question got a lot messier.

AI Doesn’t Just Help You Code — It Becomes a Dependency

When a developer pastes a prompt into GitHub Copilot or OpenAI‘s ChatGPT and accepts a suggestion, that snippet enters the codebase like any other contribution. But it’s fundamentally different from a line written by a human teammate.

You didn’t interview the model. It doesn’t have a security clearance. It doesn’t know your threat model. And critically, you have almost no visibility into the data it was trained on — which means you can’t audit whether that suggested function contains logic lifted from a GPL-licensed project, or worse, a known vulnerability.

This is not theoretical. Studies have shown that AI code assistants can regurgitate verbatim blocks from copyrighted repositories. They also hallucinate package names, suggesting libraries that don’t exist — a perfect vector for dependency confusion attacks.

The Provenance Problem Gets Worse

Traditional supply chain security relies on provenance. You know where a package came from because it has a signed manifest, a checksum, a chain of custody. With AI-generated code, the lineage is opaque. The model is a black box. You can’t ask it, “Where did you learn this algorithm?” and get a verifiable answer.

Some teams are already experimenting with adding AI model identifiers to their software bill of materials (SBOM). But that’s a nascent practice. Most organizations don’t even have a complete SBOM for their human-written dependencies, let alone one that accounts for AI contributions.

Three New Risks You Can’t Ignore

Adding AI to the build pipeline introduces categories of risk that traditional supply chain tools weren’t designed to catch.

• Invisible backdoors: A model trained on public code may have absorbed intentionally planted vulnerabilities. Researchers have demonstrated “poisoned” training data that causes models to suggest insecure code patterns when triggered by specific prompts. You wouldn’t know unless you tested every suggestion against a known exploit database.
• License contamination: AI models don’t respect licenses. They train on everything. If a model suggests code that’s functionally identical to a GPL-licensed function, your proprietary project could suddenly have a compliance problem. The legal landscape here is unsettled, but the risk is real.
• Loss of institutional knowledge: When a human writes a tricky piece of logic, they usually understand why it works. When AI writes it, the developer accepting the suggestion may not fully grasp the implications. The code passes review, but the reasoning behind it doesn’t. That’s a maintenance and security time bomb.

What Security Teams Should Do Right Now

You can’t ban AI code generation — it’s already too widespread. But you can adapt your supply chain practices to account for it.

First, treat every AI-generated code block as a high-risk third-party dependency. Subject it to the same scrutiny you’d give a new open-source library. Run static analysis. Check for known vulnerability patterns. Review it line by line, not just glance at the diff.

Second, update your software supply chain security policy to explicitly address AI contributions. Require that developers tag or comment code that was generated by an AI model. This makes it auditable. It also helps your security team spot patterns — if a particular model keeps suggesting vulnerable code, you can flag it.

Third, push your tooling vendors for transparency. Ask your AI coding assistant provider what data their model was trained on. Ask if they test for vulnerability injection. Demand a model-level SBOM. The market is young enough that customer pressure can shape the roadmap.

Accountability Is the Hardest Question

In traditional software supply chains, there’s a clear chain of accountability. If a package has a vulnerability, the maintainer is responsible. If a developer introduces a bug, the code review process catches it — or the developer owns the fix.

Who owns the bug when AI wrote the code? The developer who accepted the suggestion? The team that trained the model? The vendor who sold the tool? The legal and engineering answers are still being written. But the prudent approach is to assume that accountability ultimately rests with the human who committed the code. That means your review process needs to be robust enough to catch AI-generated mistakes before they reach production.

The software supply chain was already fragile. Adding AI to the mix doesn’t break it — but it does expose new weak points. The teams that acknowledge those weak points early, and build processes around them, will be the ones that don’t get burned when the next SolarWinds-style incident turns out to have an AI origin story.