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OpenClaw Adds VirusTotal Scanning to ClawHub: What It Means for AI Agent Security and Enterprise Risk

ByInnoVirtuoso

What happens when a wildly popular open-source AI agent becomes a favorite hunting ground for malware authors? You don’t have to guess anymore. In the wake of a sweeping campaign that hid keyloggers and info-stealers inside “helpful” extensions, OpenClaw is now hardwiring VirusTotal scanning into its ClawHub marketplace. It’s a bold, much-needed move—but is it enough to tame an attack surface that’s growing by the day?

According to reporting by CSO Online, OpenClaw’s founder Peter Steinberger, security advisor Jamieson O’Reilly, and VirusTotal’s Bernardo Quintero jointly announced a pipeline that automatically scans new and existing skills. Clean submissions are approved immediately, suspicious packages ship with warnings, and known-malicious uploads are blocked—then re-checked daily to catch newly surfaced IOCs and signature updates. This is the kind of platform-level control many security teams have been asking for as AI agents spread from hackathons to production workflows.

Here’s what changed, why it matters, and how your organization can reduce risk—today.

Reference: CSO Online coverage

The 30-second version

  • OpenClaw integrated VirusTotal scanning into ClawHub, auto-screening skills on submission and on a daily cadence.
  • The change follows a Koi Security audit that found 341 malicious skills among 2,857 reviewed as part of the “ClawHavoc” campaign—extensions disguised as crypto/YouTube tools that hid keyloggers and Atomic macOS Stealer.
  • Security firms argue AI agents like OpenClaw are “insecure by default,” with Gartner reportedly calling OpenClaw an “unacceptable cybersecurity liability” due to shadow deployments and secret leakage (per CSO Online).
  • Even without classic exploits, prompt injection can hijack agents via untrusted inputs (emails, docs, webpages). CrowdStrike warns mixed-trust data and agent permissions can enable adversary-in-the-loop actions.
  • VirusTotal scanning is a strong first line, but not a silver bullet. Enterprises should treat agent skills like third-party code, apply allowlists, segment execution, and enforce least-privilege API/OAuth scopes—immediately.

What changed: ClawHub now screens skills with VirusTotal

Per CSO Online, OpenClaw’s new pipeline does three crucial things:

  • Auto-scans incoming skills and updates using VirusTotal’s multi-engine malware analysis.
  • Triages results in-line: benign → instant approval; suspicious → visible warnings; malicious → block and prevent distribution.
  • Re-scans daily to account for updated signatures, heuristics, and community intel.

This mirrors threat-reduction steps we’ve seen at other AI code/content platforms. Hugging Face, for instance, has leaned into trust-and-safety scanning of hosted models and datasets to curb weaponized uploads (Hugging Face overview). While toolchains differ, the principle is the same: make the marketplace itself a security control, not a liability.

Why this matters: The vast majority of users will install extensions from the default marketplace. If the marketplace becomes a choke point for bad code, you shrink the effective attack surface across the entire ecosystem—especially for novice users and shadow deployments.

Why now: The “ClawHavoc” wake-up call

Koi Security’s February 1 audit examined 2,857 ClawHub skills and flagged 341 of them as malicious as part of the “ClawHavoc” campaign, CSO Online reports. These weren’t obviously shady uploads. Many wore professional veneers—crypto wallets and YouTube utilities that promised convenience or automation while concealing:

  • Keyloggers for credential theft
  • Atomic macOS Stealer (AMOS), a notorious info-stealer that targets macOS for browser data, crypto wallets, iCloud Keychain artifacts, and more
  • Data exfiltration routines to remote C2 endpoints

AMOS isn’t new, but its packaging inside tools that target AI agents is a worrying pivot. For background on AMOS and info-stealers generally, see independent coverage from vendors like Malwarebytes and others, or browse high-level info-stealer TTPs via MITRE ATT&CK.

The lesson: Bad actors go where users are. Open-source AI tools with explosive growth and simple extension systems are prime real estate for stealthy monetization schemes.

“Insecure by default”? The enterprise reality check

Security firms quoted by CSO Online warn that OpenClaw is “insecure by default,” and note Gartner’s view that OpenClaw represents an “unacceptable cybersecurity liability” because of shadow deployments that expose:

  • API keys and OAuth tokens
  • Private conversations and documents
  • Operational context and secrets embedded in prompts or skill configs

Even mature organizations struggle to inventory where AI agents run, which repos supply their skills, and which permissions are active. The result is a familiar recipe: too many privileges, too little isolation, and insufficient monitoring.

Two dynamics make AI agents uniquely risky:

  1. Inputs arrive from mixed-trust sources by design. Agents read emails, PDFs, webpages, tickets, and code—some of which are adversarial.
  2. Outputs trigger actions. Unlike a passive LLM chat, agents can invoke tools, write files, kick off pipelines, or send messages.

As CrowdStrike and others have warned, these properties create powerful “adversary-in-the-loop” opportunities. Even without a single CVE or exploit, a crafted input can redirect an agent to leak data or perform a harmful action if permissions allow it.

Resources: – CrowdStrike blog – OWASP Top 10 for LLM Applications – NIST AI Risk Management Framework

How VirusTotal-powered screening helps—and where it can’t

VirusTotal aggregates dozens of antivirus and analysis engines with static, behavioral, and reputation signals. Used at intake and continuously thereafter, it delivers key protections:

  • Blocks known malware families and commodity stealers at scale
  • Surfaces suspicious traits to slow adoption and encourage scrutiny
  • Catches time-delayed detections via re-scans as the intel graph evolves
  • Reduces the “needle-in-a-haystack” burden for platform moderators

But there are limits, and enterprises should plan around them:

  • Evasion is possible. Obfuscation, staged payloads, and environment-triggered behaviors can slip past static and sandbox analysis.
  • Supply-chain trust is broader than malware. Typosquatting, malicious updates to previously benign skills, or dependency confusion may not trip classic malware signatures.
  • Benign-but-dangerous logic won’t always flag. A “clean” extension might make unsafe API calls, over-collect data, or enable prompt-injection pathways without containing malware.

In other words: VirusTotal scanning is an essential gate, not a comprehensive assurance program.

Prompt injection: The exploit that’s not an exploit

Prompt injection isn’t a vulnerability in the traditional CVE sense. It’s an abuse pattern that exploits how agents interpret and prioritize instructions. An attacker can:

  • Bury instructions in an email, document, web page, or dataset
  • Induce the agent to exfiltrate secrets (“summarize and send to this webhook”)
  • Trick the agent into toggling settings, approving extensions, or scraping sensitive portals
  • Chain tools: read → extract → transform → transmit

This is why “mixed-trust inputs” are so dangerous. If your agent can read it, it can be influenced by it. If your agent can do it, it can be induced to do it—unless you put guardrails between inputs, policies, and actionable privileges.

Mitigations worth prioritizing: – Treat external content as untrusted by default; sandbox parsing and extraction steps. – Apply allowlists for outbound network destinations and tool invocations. – Inject policy controls and data-loss prevention checks between “reasoning” and “action.” – Train detection on anomalous tool-use sequences, not just endpoint malware signatures.

For foundational patterns, consult the OWASP LLM Top 10.

What security teams should do now

Assume you have (or soon will have) OpenClaw-based experiments—some tracked, some not. Here’s a pragmatic sequence to cut risk quickly.

1) Put guardrails around marketplaces and downloads – Block unvetted marketplace traffic at egress where feasible or route via an allowlisted proxy. – Mirror approved skills internally; require checksums and signed artifacts. – Enforce a developer attestation: who requested the skill, what’s the business purpose, what data and permissions are required?

2) Contain execution – Run agents and skills in hardened containers or ephemeral VMs with filesystem, process, and network isolation. – Use separate sandboxes for untrusted content parsing and tool execution. – Segment by environment: dev/test sandboxes should not touch prod secrets or networks.

3) Lock down identities and secrets – Rotate API keys and OAuth tokens; vault them with short TTLs and just-in-time access. – Strip secrets from prompts and configs; inject at runtime via secure sidecars. – Scope OAuth permissions to the minimum; prohibit token reuse across agents.

4) Govern skill supply chains – Maintain an allowlist of approved skills; pin to exact versions. – Require manifest metadata (publisher, repo, dependencies, permissions). – Scan dependencies (both direct and transitive); build SBOMs for reproducibility and diff-based detection of stealthy changes.

5) Instrument and observe – Centralize logs for agent actions, tool invocations, and network egress. – Alert on novel destinations, privilege escalations, or unusual data volumes. – Capture provenance: which content led to which action?

6) Put policy between “thought” and “action” – Introduce an action firewall that validates planned steps against policy (data classification, DLP, allowed domains). – Require human-in-the-loop for sensitive actions (payments, repository writes, ticket closures).

7) Test like an adversary – Red-team prompt-injection scenarios and tool-chaining abuses. – Seed canary data and watch for exfiltration attempts. – Subscribe to community intel for AI-specific TTPs; tune detections accordingly.

For developers and platform owners

If you build with or for OpenClaw, embrace a “secure-by-default” stance:

  • Principle of least privilege: Request only the permissions your skill truly needs. Design features to work with narrower scopes.
  • Transparent permissions: Explain why each permission is required in human-readable terms during install.
  • Defense in depth: Validate inputs, sanitize outputs, and prefer allowlist-based resource access.
  • Secure updates: Sign releases; publish SBOMs; avoid update mechanisms that fetch unsigned code at runtime.
  • Observability: Emit clear logs for actions, errors, network calls, and permission checks.
  • Kill-switches: Provide an easy ability for admins to disable your skill remotely if compromise is suspected.

Shadow IT is the elephant in the room

Gartner’s “unacceptable liability” language (per CSO Online) underscores what many CISOs already face: teams quietly piloting agents with powerful connectors. The blending of personal and corporate ecosystems—GitHub Copilot here, a sidecar agent there—makes it harder to draw crisp boundaries.

Practical steps: – Publish a fast-track process for sanctioned AI experiments. Make the secure path the easy path. – Offer a pre-approved toolbox: vetted skills, base containers, network policies, and CI templates. – Meet developers where they are with clear documentation, short SLAs, and lightweight reviews.

If the secure option is slow or opaque, the unsanctioned option will win.

What this means for AI ecosystems

OpenClaw’s VirusTotal move is likely the start of a broader trend: platform-level countermeasures as table stakes.

What “good” looks like at the ecosystem level: – Mandatory scanning at upload and periodic re-scan – Signed skills and verifiable builds with cryptographic attestation – Transparent moderation decisions and public safety dashboards – Granular permissioning with runtime prompts and revocation – Built-in action firewalls and DLP for agent frameworks – Bug bounties and red-team challenges tailored to prompt injection and tool chaining

Platforms that deliver these controls natively will earn enterprise trust faster than those that punt responsibility downstream.

Limitations and open questions

  • False negatives and time-to-detect: Even daily re-scans can lag behind novel evasion techniques.
  • Gray-area extensions: Clean code that behaves dangerously won’t always be flagged.
  • Dependency drift: A safe skill can become unsafe via a dependency update.
  • Accountability: Who’s responsible when a “scanned” skill still causes harm—the publisher, the platform, or the enterprise that installed it?

None of these questions are unique to AI marketplaces, but the speed and reach of AI adoption raise the stakes.

Action checklist for enterprises

  • Inventory: Identify all agent frameworks in use; map skills and permissions.
  • Block or broker: Route marketplace traffic through a controlled broker; deny direct internet installs.
  • Allowlist: Approve and pin specific skill versions; mirror them internally.
  • Isolate: Run agents in sandboxed, ephemeral environments with constrained egress.
  • Secrets: Vault and rotate credentials; never store in prompts or repos.
  • Policy guardrails: Enforce action firewalls, DLP, and human approval for sensitive operations.
  • Monitor: Centralize logs; detect anomalies in tool usage and data flows.
  • Test: Red-team prompt injection; seed canaries; practice incident response for agent abuse scenarios.
  • Educate: Train teams on mixed-trust inputs and safe prompt patterns.

Useful links

FAQ

Q: Does VirusTotal scanning make ClawHub “safe” for enterprise use? A: It’s a major improvement, not a guarantee. Scanning blocks known malware and many suspicious traits, but it can’t catch every evasion technique or “clean” but dangerous logic. Treat marketplace skills like third-party code: allowlist, pin versions, and isolate execution.

Q: How did attackers hide malware in the ClawHavoc campaign? A: Per CSO Online’s summary of Koi Security’s audit, malicious authors packaged keyloggers and Atomic macOS Stealer inside polished tools masquerading as crypto and YouTube utilities. This kind of social engineering works because users expect convenience features and may not inspect code deeply.

Q: Can prompt injection be detected by antivirus engines? A: Not reliably. Prompt injection is about manipulating agent behavior via crafted inputs, not shipping a binary payload. Mitigate it with policy guardrails, content isolation, and least-privilege tool permissions rather than relying on malware signatures alone.

Q: What immediate controls should a CISO enforce if OpenClaw is already in use? A: Block unsanctioned marketplace downloads, require internal mirrors of approved skills, isolate agent execution, vault and rotate secrets, enforce action/DLP firewalls, and centralize logging for tool invocations and egress. Start with high-impact, low-friction wins.

Q: How often should skills be re-scanned? A: OpenClaw’s move includes daily re-checks, which is sensible at platform scale. Internally, re-scan on every version change, dependency update, and periodically (e.g., daily/weekly) for high-risk categories.

Q: Are signed skills enough to trust an extension? A: Signing proves origin and integrity, not safety. Pair signatures with vetting, SBOMs, permission minimization, and runtime policy controls.

Q: What about data leakage through “benign” skills? A: A clean skill can still over-collect or mishandle data. Apply data minimization, explicit consent prompts, outbound allowlists, and DLP checks—even for approved, scanned extensions.

Q: How do I educate teams about mixed-trust inputs? A: Emphasize that any content an agent reads can influence it. Provide examples, safe prompt patterns, and require human approval for sensitive or irreversible actions triggered by external content.

Q: Is this issue unique to OpenClaw? A: No. Any AI agent or plugin ecosystem faces similar risks. OpenClaw’s VirusTotal integration is a positive example of platform-level defense that others will likely emulate.

The bottom line

OpenClaw’s integration with VirusTotal is a smart, necessary step that turns ClawHub from a passive storefront into an active security control. It will catch a lot of the low-hanging fruit and raise the cost of abuse for malware peddlers. But the hard truth remains: AI agents are powerful precisely because they straddle content and action—and that makes them inviting targets.

Treat skills as third-party code. Treat inputs as untrusted. Put policy and observation between reasoning and execution. If you do that, VirusTotal scanning isn’t your only line of defense—it’s your first.

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InnoVirtuoso

Hello there! I'm passionate content writer navigating the digital landscape. With a deep love for words and an insatiable curiosity about technology.

With 10 years of experience in IT field, I write mostly about emerging tech, sharing news, informational articles and covering other topics I find interesting.

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