AI Defense for Rogue NuGet Packages: Stop Typosquats

A malicious NuGet package can sit in plain sight for years, get thousands of downloads, and still feel “safe” to busy engineering teams. That’s not hypothetical—researchers recently found a rogue package impersonating a popular .NET tracing library that stole cryptocurrency wallet files and passwords, and it reportedly stayed available for nearly six years.

Most companies get this wrong because they treat open-source risk as a one-time checkbox: “We scanned dependencies.” The reality is simpler—and harsher. Software supply chain attacks don’t need a zero-day when they can slip a lookalike package into your build. This incident is a clean reminder of why the “AI in Cybersecurity” conversation isn’t about hype; it’s about keeping up with attackers who automate deception.

What happened: a fake NuGet package that quietly stole wallets

The core issue was straightforward: a NuGet package named Tracer.Fody.NLog posed as if it were part of the legitimate Tracer.Fody ecosystem. It mimicked the real maintainer’s identity by using a publisher name that differed by one letter (a classic typosquatting tactic), increasing the odds that developers would trust it during a quick install.

Researchers reported three details that should make any .NET shop uneasy:

• Longevity: The package was published in February 2020 and remained in the repository for years.
• Adoption: It was downloaded 2,000+ times, with recent downloads still occurring.
• Payload: It behaved like a tracing integration on the surface, but it scanned for Stratis wallet files, extracted wallet data and passwords, and attempted to exfiltrate them to attacker-controlled infrastructure.

If you’re thinking, “We don’t use crypto wallets, so we’re fine,” don’t get comfortable. Wallet theft is just the monetization choice here. The same pattern works for credential theft, token harvesting, source code exfiltration, CI runner takeover, or implanting backdoors into internal apps.

Why this specific technique works so often

Typosquatting succeeds because development is optimized for speed:

• Engineers copy/paste package names from memory or chat threads.
• Reviewers focus on business logic, not the dependency graph.
• Build pipelines restore packages automatically, and “it builds” becomes the proof of safety.

Attackers don’t need to beat your endpoint detection if they can convince your build to ship their code.

The stealth mechanics: how the malware hid in normal-looking code paths

Answer first: The package hid malicious behavior behind routines that run during normal execution, while avoiding obvious crashes or logs.

According to the research, the rogue DLL embedded behavior that:

1. Looked in the default Stratis wallet directory on Windows.
2. Read *.wallet.json files.
3. Grabbed wallet data and passwords (including in-memory password access).
4. Exfiltrated everything to remote infrastructure.

Two stealth tactics matter for defenders:

1) “Looks normal” helper functions

The malicious routine was reportedly tucked into a generic helper like Guard.NotNull—the kind of method that gets called everywhere. That means:

• Execution is frequent.
• Behavior blends into legitimate call stacks.
• A quick skim of the code can miss it, especially when the name is boring.

2) Cyrillic lookalike characters

Using visually similar characters makes searching and code review harder. It also trips up simplistic detection rules that assume ASCII-only identifiers.

3) Silent exception handling

Catching exceptions without surfacing errors is a gift to attackers. Even if exfiltration fails, the host app continues running “fine.” The compromise becomes a billing mystery, a missing wallet mystery, or a “why did our build agent talk to that IP?” mystery.

A supply chain attack that fails loudly gets removed. A supply chain attack that fails quietly gets repeated.

Why traditional AppSec controls miss this (and where AI helps)

Answer first: Traditional controls focus on known bad signatures and periodic scans; AI helps by spotting weirdness in identity, behavior, and dependency context—even when the code is “new.”

Many teams rely on a mix of:

• SCA scanners (good, but often CVE-centric)
• basic allow/deny lists
• manual review (rarely scales)
• perimeter/network tools that don’t understand builds

Those controls struggle with typosquatting because:

• There may be no CVE.
• The package may look “popular enough.”
• The malicious logic can be small and buried.

AI-driven threat detection earns its keep by correlating weak signals that humans ignore.

AI signal #1: Package identity anomalies

AI models can score the probability of impersonation using features like:

• name similarity to high-trust packages
• publisher similarity (one-letter differences)
• suspicious timing patterns (publish bursts, version jumps)
• metadata mismatch (description/repo mismatch, inconsistent authorship)

This is the same idea as fraud detection in payments: a single odd detail isn’t proof, but a cluster of odd details is.

AI signal #2: Behavioral anomalies during build and runtime

Static scanning is necessary; it’s not sufficient. The more reliable question is:

“What does this dependency do when it runs?”

AI-assisted analysis can highlight behaviors that don’t fit the declared purpose of a tracing/logging library, such as:

• file access to wallet directories
• reading JSON secrets from user profile paths
• DNS or HTTP calls to unusual endpoints
• attempts to capture in-memory secrets

For many organizations, the win is speed: instead of waiting for an incident report, you get near-real-time detection in CI or pre-production.

AI signal #3: Graph context across your dependency tree

A single new package may not look alarming. But when you combine:

• a new logging/tracing integration
• a new transitive dependency chain
• network egress during unit tests

…you have a pattern. AI is good at pattern detection across noisy graphs, especially when tuned to your organization’s baseline.

Practical playbook: how to reduce NuGet typosquatting risk this week

Answer first: You’ll cut most of the risk by tightening package provenance, enforcing dependency policies in CI, and monitoring for anomalous behavior—then using AI to prioritize what humans review.

Here’s a pragmatic checklist that works even when teams are busy.

1) Lock dependencies and require explicit upgrades

• Use lock files and deterministic restores.
• Require PR-based upgrades for dependency changes.
• Block “floating” versions in production builds.

Why it matters: typosquatting often succeeds through accidental adds or broad version ranges.

2) Add impersonation checks as a policy, not advice

Implement CI gates that fail builds if:

• a package name is highly similar to a known internal-approved list
• publisher identity differs from expected for critical packages
• a “new” package appears in a sensitive repo without a ticket/approval

AI improves this step by ranking which similarities are likely benign vs deceptive.

3) Treat “utility” packages as high-risk

Attackers like:

• tracing/logging
• argument validation
• async helpers
• build tooling
• VS extensions

Why? These packages run early and often, and they’re rarely scrutinized. Give them higher scrutiny than business libraries.

4) Monitor CI runners like production assets

Your CI environment holds:

• signing keys
• deploy tokens
• artifact credentials
• internal repo access

At minimum:

• restrict outbound network egress from runners
• alert on new external destinations
• isolate runners per project sensitivity

AI-powered SOC workflows help here by correlating “new dependency added” + “new outbound call from build” into a single incident.

5) Use behavior-aware scanning, not only CVE scanning

CVE-based SCA answers: “Is this known vulnerable?”

You also need: “Is this acting like malware?”

Look for tools and processes that support:

• code similarity to known malicious families
• suspicious string/IOC patterns
• sandbox execution of install/build steps
• runtime behavior profiling

This is where AI in cybersecurity is most practical: it can prioritize what to sandbox and what to escalate.

“People also ask” (and what I tell teams)

How can a NuGet package steal data without admin rights?

Most sensitive developer data lives in user-space: %APPDATA%, browser profiles, SSH keys, tokens, and app configuration. Malware doesn’t need admin if the user has access—which build agents and developers often do.

Why would attackers target a tracing library?

Tracing/logging runs everywhere. It’s present in dev, CI, staging, and production. A malicious tracing dependency is a quiet passenger with broad reach.

Is AI required to stop supply chain attacks?

Not required, but it’s increasingly hard to do at enterprise scale without it. Attackers automate publication, obfuscation, and iteration. Human review alone doesn’t match that throughput.

What this means for the “AI in Cybersecurity” series

Answer first: AI is most valuable when it reduces the time between “malicious package introduced” and “package removed from your environment” from weeks to minutes.

This NuGet incident is a reminder that supply chain security is now a detection-and-response problem, not only a prevention problem. You still need guardrails (approved sources, lock files, CI policies). But you also need systems that notice when a “logging helper” starts behaving like a wallet stealer.

If you’re building an AI-driven security program for 2026, I’d put software supply chain monitoring near the top of the list. It’s one of the few places where better detection directly prevents real loss: drained wallets, stolen tokens, poisoned builds, and compromised customer environments.

Where do you feel the most friction right now—locking dependencies, reviewing new packages, or getting actionable alerts from your build pipeline? That answer usually points to the first AI automation worth funding.