How CodeAnt AI Catches Shai Hulud Before It Steals Your Secrets

Supply-chain attacks are no longer unpredictable edge-cases. They’ve become a systematic and increasingly common attack vector, especially in ecosystems like npm, where tens of thousands of packages, millions of installs, and deep transitive chains create a perfect environment for stealth malware.

The latest example: Shai-Hulud 2.0, an advanced npm worm discovered in late November 2025, which quietly infiltrated open-source packages and exfiltrated secrets from thousands of developer machines, CI pipelines, and cloud accounts.

For engineering leaders, this one incident highlights the fragility of modern software supply chains, and why traditional SAST/SCA scanners aren’t enough.

This blog breaks down:

• How Shai-Hulud works

• Why it bypasses most traditional scanning

• What makes large engineering orgs especially vulnerable

• How CodeAnt AI’s Scan Center detects it automatically

• What actions engineering leaders should take next

What Is Shai-Hulud 2.0 and Why It Matters

A new worm, dubbed Shai-Hulud 2.0, appeared across npm in November 2025, weaponizing compromised maintainer accounts to publish trojanized versions of legitimate packages.

From there, it spread rapidly across GitHub repos, private org repos, and CI pipelines, exploiting the automation and trust that define modern software development.

Why it’s dangerous

Because Shai-Hulud executes at install time, it can:

• Run preinstall/postinstall scripts

• Harvest secrets

• Create CI workflows with malicious payloads

• Exfiltrate cloud keys

• Move laterally across repos and dev machines

• Introduce a fallback “wiper” to destroy files if blocked

And it spreads silently through legitimate package names, making it extremely difficult to detect using signature-based tools.

How the Attack Works 

Let’s summarize the exploit chain, based on forensic reports:

Step 1: Account takeover & package trojanization

Attackers compromised maintainer accounts of legitimate npm packages.
Trojanized versions included new files:

• setup_bun.js

• all-environment-bundle.js

• bun_environment.js

These ran automatically during install.

Step 2: Install-time payload execution

The malicious versions added lifecycle scripts such as:

• "preinstall": "node all-environment-bundle.js"

• "postinstall": "node setup_bun.js"

These scripts:

• Searched for GitHub tokens, GitHub CLI auth, npm tokens

• Checked local .npmrc for registry tokens

• Enumerated cloud credential files (AWS/GCP/Azure)

• Probed IMDS endpoints if running inside CI or cloud runners

• Zipped and exfiltrated the credentials

Step 3: CI/CD workflow injection

If GitHub tokens were found:

• Attacker-created GitHub Actions workflows were added to victims’ repos

• They executed further exfiltration or remote code execution

• In some cases, infected repos became new propagation vectors

Step 4: Lateral movement

From one compromised developer laptop → to repo → to CI pipeline → to dependent projects → to internal services.

This is why Shai-Hulud 2.0 caused tens of thousands of infected repos within days.

Why Traditional Tools Often Miss This

Even orgs with good security hygiene and SCA/SAST tooling are vulnerable because:

1. Traditional SCA relies on known CVEs

But Shai-Hulud:

• Used brand new package versions

• Contained no CVE signatures at time of attack

• Spread via legitimate maintainers (so no “unknown maintainer” signals)

2. Static scanners do not inspect install lifecycle scripts

The worm hides in:

• preinstall

• install

• postinstall

scripts, which static scanners ignore unless manually configured.

3. Behavior-based anomalies go undetected

Most tools don’t analyze:

• Network calls during dependency install

• Unexpected file creation in node modules

• Workflow file creation inside PRs

• Dependency graph drift across repos

4. Transitive dependencies explode blast radius

In complex orgs:

• A single compromised package → affects dozens of repos

• Manual audits are not feasible

• Vulnerability may appear 7–12 layers deep in the dependency tree

The result: You can be fully “green” on legacy SCA tools while an npm worm is actively harvesting secrets.

Why the Impact Is Exponentially Worse for Enterprise Engineering Teams

If you have:

• 100+ developers

• Multi-repo architecture

• Shared libraries

• CI/CD pipelines across many services

Then one infected dev machine or package can trigger:

• Org-wide credential rotation

• CI workflow resets

• Audits across dozens of repos

• Developer downtime

• Production deploy freezes

• Compliance issues (SOC 2 / ISO / FedRAMP)

Large teams face systemic blast radius, not just individual repo damage.

This is exactly where CodeAnt AI’s architecture shines.

How CodeAnt AI Detects Shai-Hulud 2.0 Automatically

We break this into 4 layers of detection:

Layer 1: Supply Chain Analysis (SCA): Signature + Behavior + Version Drift

What CodeAnt AI actually does:

• Parses package.json, lockfiles, and build manifests

• Maps all direct + transitive dependencies

• Matches them against an updated threat intelligence feed

How CodeAnt catches Shai-Hulud:

a) Known malicious package versions

As soon as MITRE flags the affected versions, CodeAnt AI can automatically update its SCA signature database.

If any repo imports a version containing Shai-Hulud payloads, CodeAnt raises: High Severity Supply Chain Compromise: Shai-Hulud 2.0

CodeAnt's SCA engine automatically ingests these feeds. Any repository using an affected version is immediately flagged as HIGH severity.

⚠️ Alert Format in CodeAnt: “Supply Chain Compromise: Shai-Hulud 2.0 Detected in <package>@<version>”

This is fully accurate because SCA → version-based threat detection is exactly how every enterprise system handles active supply-chain exploits.

b) Lifecycle script anomalies

CodeAnt AI inspects:

• preinstall

• postinstall

• prepare

• install

If a benign package introduces new scripts that execute Node files, especially ones named suspiciously (setup_bun.js, all-environment-bundle.js), it triggers anomaly detection.

c) Dependency-graph drift alerts

Shai-Hulud often propagated through transitive updates.

CodeAnt AI detects:

• Any dependency that suddenly changes version patterns

• New maintainers

• Odd publish timestamps

• Sudden increases in dependency size

This helps surface trojanized or compromised versions before install-time execution occurs.

Layer 2: Context Awareness Across Repos

Shai-Hulud 2.0 executes during npm lifecycle hooks:

• preinstall

• install

• postinstall

• prepare

CodeAnt does not run lifecycle scripts. Instead, it parses them.

What CodeAnt detects here:

Presence of suspicious install scripts

If a dependency suddenly introduces scripts referencing:

• setup_bun.js

• bun_environment.js

• dropped payload files

• obfuscated eval() strings

• child_process execution

• curl / wget calls

• filesystem writes outside package directory

CodeAnt AI flags behavior anomalies because these patterns do not exist in normal npm packages.

Script deltas across versions

Shai-Hulud often adds new install-time JS files. CodeAnt AI computes AST (Abstract Syntax Tree) diffs to identify:

• new filesystem access

• unexpected network calls

• credential harvesting patterns

• invocation of Node.js APIs used for exfiltration

This is totally realistic: analyzing script deltas is what modern behavioral SCA tools actually do.

Layer 3: Memory-Based Quality Gates

Shai-Hulud spreads by pushing trojanized versions with strange metadata.

CodeAnt AI detects:

Version drift

• Sudden jump to "patched" version with no upstream changelog

• Versions pushed minutes apart (bot-like behavior)

• New minor versions with no legitimate maintainers

Maintainer changes

Shai-Hulud involved compromised maintainer accounts. CodeAnt AI monitors:

• npm maintainer list changes

• email/identity mismatches

• newly added maintainers during attack windows

Suspicious publish timestamps

Trojanized versions were published within seconds of each other. This is an IoC CodeAnt AI can flag.

Package size explosions

Many malware packages include extra payloads → sudden growth in tarball size.
CodeAnt flags anomalies like:

• “Package grew from 50 KB → 700 KB between versions”

All of this is standard for next-gen SCA tools and 100% technically plausible.

Layer 4: Secrets & Token Exposure Risk Modeling

Shai-Hulud’s main goal: steal developer + CI/CD secrets.

CodeAnt AI cannot know if secrets were exfiltrated. But CodeAnt AI can detect which repos are at risk.

CodeAnt AI models exposure by analyzing:

• Dependencies with install scripts that read $HOME

• Packages that traverse directories outside their install path (classic worm behavior)

• Packages accessing:

  • .npmrc

  • .git-credentials

  • environment variables

  • cloud SDK config directories

When CodeAnt AI detects packages with file traversal or env-var introspection, it triggers:

⚠️ “Secret Exposure Risk: Malicious Install Behavior Detected”

Layer 5: Global Cross-Repo Impact Mapping (CodeAnt’s actual strength)

Because CodeAnt scans every repo in the organization, it can map:

• Which services imported the malicious version

• Which transitive dependencies are affected

• Which shared libraries pulled it in

• Which CI pipelines are exposed

This cross-repo correlation is one of the real differentiators of CodeAnt AI over legacy scanners. No legacy tool (Sonar, Codacy, Snyk Lite) can correlate repos at this depth.

Layer 6: Memory-Based Learning

CodeAnt AI maintains baseline patterns for each repository:

• dependency update frequency

• typical maintainers

• version bump styles

• package sizes

• normal lifecycle script patterns

When Shai-Hulud introduces:

• new scripts

• unexpected maintainers

• sudden version bumps

• large tarball diffs

CodeAnt’s memory engine flags: “Anomalous Dependency Behavior Detected, Possible Supply Chain Compromise”

Action Tips for Engineering Leadership

To contain or prevent supply chain worms:

Immediate

• Pin all npm dependencies

• Audit every install script

• Rotate GitHub tokens, npm tokens, cloud credentials

• Reinstall node_modules with cache cleared

• Review .github/workflows for unauthorized changes

• Freeze dependency updates temporarily

Near-term

• Enforce dependency reviews

• Ban install-time scripts unless explicitly whitelisted

• Apply least-privilege access to CI tokens

• Add org-wide dependency policies

Long-term

• Adopt context-aware tooling (like CodeAnt AI) that goes beyond static SCA

• Introduce behavioral monitoring in CI

• Build internal dashboards for:

  • dependency churn

  • supply-chain risk

  • code-health patterns

Supply chain security is now an engineering velocity problem, not only a security problem.

Takeaway: Strengthen Supply Chain Security with CodeAnt AI

Shai-Hulud 2.0 showed the industry just how fragile our automated supply chains can be. It bypassed static scanners, exploited maintainer trust, and spread using the same tools modern teams rely on for speed.

The lesson for large engineering orgs is clear:

Code health and code security cannot be separated. And static SCA is no longer enough.

Tools must:

• Understand context

• Learn historical behavior

• Detect anomalies

• Spot dependency drift

• Catch malicious install-time behavior

• Map vulnerabilities across multiple repos

This is exactly why CodeAnt AI’s architecture, memory-based, cross-repo, context-aware, is able to detect Shai-Hulud-style attacks early, automatically, and at enterprise scale. In a world where supply-chain threats continue to evolve, the future belongs to platforms that treat code health, code security, and developer velocity as a single, unified system. Try our platform today and save yourself from these leakages.