AI Defense Against Rogue NuGet Typosquat Attacks

A malicious NuGet package sat in plain sight for nearly six years—and still racked up 2,000+ downloads. That’s not a “someone clicked a sketchy attachment” story. It’s a software supply chain story, where normal developer behavior (adding a dependency) can quietly turn into credential theft and data exfiltration.

The package, Tracer.Fody.NLog, impersonated the legitimate .NET tracing library Tracer.Fody by mimicking the maintainer name with a one-letter difference. Once installed, it searched Windows for Stratis cryptocurrency wallet files (*.wallet.json) and siphoned wallet data and passwords to attacker infrastructure. The most frustrating part: it was engineered to look like boring helper code and fail silently, so apps kept running while secrets leaked.

This post is part of our AI in Cybersecurity series, and I’m going to take a clear stance: manual review and “we trust NuGet” are not defenses. If you want to reduce real risk from typosquatting and malicious packages, you need automated controls—and increasingly, AI-driven threat detection and anomaly analysis—to spot what humans and static rules miss.

What happened: a typosquat that stole wallet data

Answer first: The attacker published a NuGet package that looked like a common tracing/logging integration but actually contained a wallet stealer that exfiltrated Stratis wallet data.

Researchers identified a malicious NuGet package named Tracer.Fody.NLog. It masqueraded as the popular tracing library Tracer.Fody and copied the maintainer’s identity closely enough to pass quick inspection: csnemes vs csnemess. That tiny detail matters because dependency selection often happens under time pressure—especially when teams are trying to ship before year-end freezes (which, in December, is basically everyone).

Once referenced by a project, the embedded DLL scanned the default Stratis wallet directory on Windows:

• %APPDATA%\StratisNode\stratis\StratisMain

It then:

1. Located *.wallet.json files
2. Extracted wallet data
3. Captured passwords (including in-memory values)
4. Exfiltrated the bundle to attacker-controlled infrastructure (reported as Russia-hosted)

The design choice that should worry every engineering leader: exceptions were silently caught, so even failed exfiltration wouldn’t break the host application or throw obvious errors.

Why this one stayed alive so long

Answer first: It blended in socially (identity mimicry) and technically (code hiding tricks), which defeats casual review.

This attack used a mix of techniques that are increasingly common in open-source ecosystem abuse:

• Maintainer impersonation with a one-character difference
• Cyrillic lookalike characters in source code (easy to miss in reviews)
• Malicious logic buried inside a generic helper function like Guard.NotNull

That last point is particularly nasty. Developers expect Guard.NotNull-style helpers to run constantly across code paths. So the malware gets execution opportunities without adding suspicious “on startup” hooks.

Why this is a supply chain risk, not a crypto-only problem

Answer first: Even if you don’t touch cryptocurrency, the same tactics apply to packages that can access tokens, credentials, logs, config files, and CI secrets.

It’s tempting to file this under “crypto theft,” but that’s the wrong mental model. The technique scales far beyond wallets:

• Logging and tracing packages can touch request payloads, headers, API keys, and PII.
• Utility libraries run everywhere—meaning attackers get high execution frequency.
• Build-time tooling (like Fody-related ecosystems) can influence outputs in ways that are hard to trace.

If your organization ships .NET services, your highest-value secrets are usually:

• Cloud credentials and temporary tokens
• Service-to-service auth material
• Customer data in logs (yes, it still happens)
• CI/CD secrets and signing keys

The uncomfortable reality: dependency compromise is often quieter than endpoint compromise. There’s no phishing email, no user training angle, and often no clear “patient zero.” There’s just a pull request that looks like a small improvement.

Why December amplifies the risk

Answer first: Holiday release pressure + reduced staffing makes “small dependency changes” more likely to slip through.

Late December is a perfect storm:

• Smaller on-call rotations
• More “quick fixes” before code freezes
• Developers working across time zones and PTO schedules

Attackers know this. Typosquats don’t need to infect everyone; they only need to infect a few teams with real assets.

Where AI-driven threat detection actually helps (and where it doesn’t)

Answer first: AI helps most when it’s used for behavioral anomaly detection and relationship analysis across packages—not for “guessing if code is bad.”

Most teams already have some static checks: version pinning, allowlists, or vulnerability scanners. Those are necessary, but they’re not sufficient against a package that:

• Looks legitimate
• Has a plausible name
• Doesn’t exploit a known CVE
• Hides malicious behavior behind normal execution

This is where AI in cybersecurity becomes practical—not hypey. AI systems can be trained to detect patterns of abuse across ecosystems, codebases, and runtime behavior.

1) AI for package ecosystem anomaly analysis

Answer first: AI can flag suspicious publishing and identity patterns that are too subtle for manual review.

Examples of signals that ML models handle well:

• Maintainer names that are near-matches of trusted authors (edit distance, homograph detection)
• Packages that mimic popular names but show unusual metadata patterns
• Download spikes that don’t match historical adoption curves
• New versions that add network behavior unrelated to package purpose

A solid system doesn’t just say “malicious.” It produces an explainable risk score with reasons like:

• “Maintainer name is a near-duplicate of known author”
• “Package contains unexpected filesystem access patterns for its declared category”
• “Outbound network destinations are new/rare across this ecosystem”

2) AI for code-level behavioral intent, not code style

Answer first: You’re looking for intent mismatches: tracing libraries shouldn’t scan wallet directories.

Static AI analysis is strongest when it focuses on semantic mismatches:

• A tracing/logging integration touching %APPDATA% wallet paths
• A helper method used widely that also performs encoding + HTTP POST
• Obfuscated strings or lookalike identifiers clustered around network calls

This isn’t about “AI reading your code like a human.” It’s about AI detecting when code behavior doesn’t align with the library’s declared job.

3) AI at runtime: catching exfiltration attempts that compile-time missed

Answer first: Runtime AI can detect abnormal egress and file-access sequences even if the package passed review.

If a malicious package makes it into an internal build, you still get another shot if your runtime monitoring is strong:

• Detect processes accessing sensitive directories followed by outbound calls
• Identify rare destination IPs/domains for that service
• Correlate activity across hosts: “why did three build agents contact the same new endpoint?”

The key is correlation. Humans can’t manually connect those dots fast enough at scale.

A practical defense plan for .NET teams using NuGet

Answer first: Reduce dependency risk by combining policy, automation, and AI-based detection across the SDLC.

If you’re responsible for engineering or security in a .NET environment, here’s what I’ve found works without turning development into molasses.

Baseline controls (do these first)

• Pin dependencies (avoid floating versions in production builds)
• Require lock files and verify them in CI
• Block direct installs from developer laptops into main branches (use PR-based updates)
• Enforce repo provenance checks: package owner, signing status (where available), and known publisher history
• Maintain an internal approved package list for high-risk categories (auth, crypto, logging, tracing)

Add guardrails that specifically target typosquatting

• Homograph detection for package names (Cyrillic/Unicode lookalikes)
• Similarity matching for maintainers/publishers (one-letter impersonations)
• Alerts for “too-close” names to your most-used packages

If your team has ever installed the wrong Docker image tag or mistyped a GitHub org name, treat typosquatting as inevitable—not hypothetical.

Where AI-driven security tools fit best

• Repository monitoring: continuously score new/updated packages and publisher identities
• Behavioral analysis: detect “this library category shouldn’t do that” actions (filesystem + network + process)
• SOC automation: auto-triage dependency alerts by correlating CI events, build artifacts, and runtime telemetry

A useful rule: if your control depends on a human noticing a one-letter difference in a package author name, it’s not a control.

“People also ask” (the questions your team will raise)

Can’t we just rely on vulnerability scanners?

Answer first: No—typosquatting malware often isn’t a known vulnerability, it’s a malicious feature.

Traditional scanners are great at detecting known CVEs in known versions. They’re weaker against a brand-new malicious package that behaves “normally” from a dependency graph perspective.

Would signing packages solve this?

Answer first: It helps, but only if you enforce trust policies and verify signer identities.

Signing improves provenance, but attackers can still publish unsigned packages that teams accidentally install, or compromise a legitimate publisher. Signing is one layer—not the whole story.

Why are tracing/logging packages frequent targets?

Answer first: They run everywhere and touch valuable data.

Anything that executes across most requests, with access to config, environment variables, headers, and payloads, is a perfect hiding place.

What to do next if you suspect you pulled a rogue NuGet dependency

Answer first: Treat it like credential exposure: isolate, rotate, and audit.

If there’s any chance a malicious package made it into your builds:

1. Identify affected repos, branches, and build pipelines (search dependency manifests)
2. Rebuild from a known-good commit after removing the dependency
3. Rotate secrets that could be exposed on affected systems (CI tokens, cloud creds)
4. Inspect egress logs for unusual outbound destinations during build and runtime
5. Hunt for artifacts: unexpected DLLs, suspicious helper functions, unusual Unicode in source

The goal isn’t just cleanup—it’s learning where your process allowed the package in.

The bigger lesson for AI in cybersecurity

Rogue packages like this are a reminder that software supply chain security is an operations problem, not a one-time audit. The organizations doing well here aren’t manually reviewing every dependency. They’re using automation and AI-driven threat detection to monitor ecosystems, score anomalies, and shut down risky behavior fast.

If you’re building an AI in Cybersecurity roadmap for 2026, put dependency risk on it. Typosquatting isn’t going away, and attackers are getting better at blending into legitimate developer workflows.

What would change in your organization if every new dependency had to “prove” it behaves like the category it claims to be?