AI vs. Typosquatting: Stop Rogue NuGet Packages

A malicious NuGet package impersonated a legitimate .NET library and stayed available for nearly six years. It wasn’t “zero-day wizardry.” It was simple supply chain deception: a one-letter publisher lookalike, familiar package naming, and malware hidden where most reviewers won’t look.

That’s why this incident matters beyond cryptocurrency. If a rogue package can sit in a mainstream repository for years and still get fresh downloads in late 2025, the real issue is how enterprises decide what code is “trusted.” And that’s exactly where AI in cybersecurity earns its keep: finding the patterns humans miss across massive dependency graphs, build logs, and runtime signals.

This post breaks down what happened with the Tracer.Fody impersonation, why it worked, and the practical controls—especially AI-assisted ones—that stop typosquatting and data theft before it hits production.

What happened: a “normal” NuGet add that wasn’t normal

A typosquatted NuGet package called Tracer.Fody.NLog posed as an integration for the popular .NET tracing library Tracer.Fody. The attacker also mimicked the legitimate maintainer’s identity by using a publisher name that differed by a single letter (a classic “looks right at a glance” trick).

Once referenced by a project, the package executed wallet-stealing behavior. According to the research described in the source article, the embedded DLL scanned a default Stratis wallet directory on Windows, collected wallet files (for example *.wallet.json) and associated secrets (including passwords from memory), then attempted to exfiltrate the data to attacker-controlled infrastructure.

Two details should make security teams uncomfortable:

• The package reportedly remained available for nearly six years.
• It had ~2,000 downloads, including recent activity in the last several weeks.

That pattern—low but persistent downloads over a long time—is exactly what makes supply chain attacks so effective: they don’t need mass distribution. They need the right victim.

Why it slipped past casual review

The attack relied on “boring” tactics that repeatedly work in software ecosystems:

• Typosquatting and impersonation: maintainer and package naming that appear legitimate.
• Code obfuscation by familiarity: hiding malicious routines inside generic helper methods (for example, something like Guard.NotNull) that’s naturally invoked.
• Lookalike characters: using Cyrillic or visually similar Unicode characters to make suspicious code harder to spot.
• Silent failure: catching exceptions and continuing execution so the host app “works,” reducing investigation triggers.

This is also why “we review our dependencies” is often aspirational. Human review is slow, inconsistent, and fragile against tricks designed to waste reviewer attention.

The bigger lesson: software supply chains fail at the trust boundary

Most teams treat package repositories like a grocery store: if it’s on the shelf, it’s probably safe. The reality is closer to a flea market.

NuGet, npm, PyPI, RubyGems, Maven—every ecosystem has the same structural problem:

1. Publishing is easy (by design).
2. Names are cheap (attackers can register convincing variants).
3. Consumers automate installs (CI/CD pulls dependencies at machine speed).
4. Impact is transitive (one compromised package can flow into many apps).

So the question isn’t “How did one malicious package get in?” It’s: What’s your control point when your developers add a dependency at 4:57 PM on a Friday?

In the AI in Cybersecurity series, I keep coming back to one theme: security has to operate at the same speed as engineering. AI-based threat detection is valuable because it scales review and monitoring to match the real volume of changes.

Where AI actually helps: detection signals humans won’t correlate

AI shouldn’t be a buzzword stapled onto a scanner. The practical value is correlation: combining weak signals that are individually explainable but collectively suspicious.

Here are the AI-friendly signals in this incident that a strong program can flag early.

1) Repository and identity anomalies

Answer first: AI can spot impersonation patterns across package ecosystems by learning what “normal” maintainer behavior looks like.

Examples of high-signal anomalies:

• Publisher name differs by one character from a known maintainer.
• New package appears that “matches” a popular library naming pattern.
• Maintainer has limited history, few packages, or unusual release cadence.

A simple ruleset can catch some of this, but ML helps when attackers vary tactics (spacing, Unicode, suffixes like .NLog, etc.).

2) Static code signals (without pretending static analysis is enough)

Answer first: AI-assisted code analysis flags unusual file access and secret-handling patterns for the declared purpose of the package.

If a tracing/logging integration library touches:

• %APPDATA% wallet paths
• JSON wallet artifacts
• credential or seed phrase patterns
• direct outbound network calls to hard-coded endpoints

…that’s a purpose mismatch. Tracing libraries should be instrumenting calls, not enumerating wallet directories.

AI helps by classifying behavior relative to intent. Classic static analysis often produces noisy results; an AI layer can prioritize findings by comparing to known-good patterns from similar packages.

3) Unicode and obfuscation detection

Answer first: AI excels at detecting code that “reads normal” but tokenizes oddly.

Lookalike characters and subtle obfuscation techniques create artifacts in:

• token frequency
• identifier entropy
• uncommon Unicode ranges
• inconsistent naming styles inside a single file

This is exactly the kind of pattern detection ML models do well at—especially when paired with deterministic checks that simply reject suspicious Unicode in identifiers for security-sensitive repos.

4) Runtime behavior (the most underrated control)

Answer first: behavior-based detection catches what code review misses, especially when malware activates only during execution.

In enterprise environments, you can instrument build agents, dev machines, and production workloads to detect:

• unexpected file reads from wallet directories
• processes loading a new DLL and immediately enumerating %APPDATA%
• suspicious outbound requests from build/test processes

AI becomes useful here by reducing alert fatigue. A model that understands “normal build behavior” can flag the one pipeline that suddenly starts reaching out to a rare external IP or reading non-build artifacts.

A practical defense plan for .NET teams using NuGet

You don’t need a research lab. You need a few controls that make this class of attack expensive.

1) Lock dependencies like you mean it

Answer first: deterministic builds reduce surprise upgrades and make rogue additions easier to spot.

Implement:

• Package lock files and CI enforcement
• Version pinning for production services
• A policy that disallows floating versions in critical apps

This doesn’t stop a developer from adding the wrong package, but it prevents “silent drift” and makes reviews clearer.

2) Add a package allowlist for high-risk environments

Answer first: the highest-confidence control is restricting what can be installed.

For regulated workloads or high-value systems:

• Mirror approved packages to an internal feed
• Require security review for new package additions
• Block direct restores from public feeds in production build pipelines

Yes, it’s stricter. That’s the point.

3) Run AI-assisted supply chain scanning at PR time

Answer first: catching the bad package before merge is cheaper than incident response.

At pull request or pipeline restore time, scan for:

• typosquatting likelihood (name similarity, publisher similarity)
• suspicious Unicode
• new outbound network indicators in code
• file system access inconsistent with package category

The win is speed: developers get feedback while they can still change course.

4) Monitor egress and treat developer tooling as production-grade risk

Answer first: if your build agents can reach the internet freely, malware will use them.

Controls that work well:

• Restrict outbound traffic from CI/build runners to known endpoints
• Alert on new/rare destinations from build and test jobs
• Record dependency restore events and correlate them with new egress patterns

This is where AI shines operationally: it can baseline “normal” for each pipeline and flag deviations in minutes.

5) Don’t ignore “low download count” packages

Answer first: low popularity is not reassurance; it’s often a targeting signal.

A package with a few thousand downloads over years can still compromise:

• internal line-of-business apps
• crypto-related projects
• fintech prototypes
• developer machines holding tokens and credentials

Attackers don’t need scale when the payout is high.

“Would we catch this?” A quick self-check for security leaders

If you’re responsible for AppSec or security operations, here’s a blunt test I use:

• If a developer adds a new NuGet package today, do you get a machine-readable event for it?
• Can you answer within 30 minutes: which apps pulled it, which pipelines restored it, and who approved it?
• Do you have controls that detect purpose mismatch (a logging library reading wallet files)?
• If exfiltration fails silently, would your monitoring still catch the attempt?

If those answers are “no,” you don’t have a supply chain program yet—you have best intentions.

What this means for AI in Cybersecurity going into 2026

AI is most useful when it’s doing what humans can’t: continuous correlation across identity, code, and behavior. Typosquatting attacks work because they sit in the gaps between teams—developers see “a package,” security sees “a dependency list,” operations sees “some network traffic.” Nobody sees the full story.

The Tracer.Fody impersonation is a clean example of why automated detection matters. When a repository artifact can masquerade as normal for years, the only sustainable answer is to combine preventative controls (allowlists, pinning, internal feeds) with AI-driven anomaly detection across the software supply chain.

If you’re building an AI in cybersecurity roadmap for 2026, I’d start here: pick one ecosystem (NuGet is a great candidate), instrument it end-to-end, and measure how quickly you can detect and block a suspicious package—before it steals anything worth stealing.