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Security audit

Cloudforge Automatio

Security checks for vulnerabilities and agentic risk

Overview

This Markdown skill is mostly coherent as a cloud infrastructure automation guide, but it includes high-impact production deployment and failover patterns without consistently clear approval and dependency safeguards.

Review this before installing if you might give the agent real cloud or CI credentials. Use it only in tightly scoped repos and accounts, require human approval before any Terraform apply, failover, DNS, or cost-optimization action, pin CI dependencies/actions, and prefer sandbox or non-production environments until the workflow is hardened.

Vulnerability Patterns
  • Insecure DependenciesIntroduces malicious components through unsafe dependency sources
  • Skill Instruction HijackingAlters the agent's session goals or safety constraints when the skill loads
  • Agent Memory PoisoningWrites attacker-controlled rules into memory that affect later sessions
  • Remote Payload Retrieval and ExecutionFetches external code whose behavior can change after review
  • Embedded Malicious CodeShips malicious scripts inside the skill and executes them locally
Findings (1)

T08 · Insecure Dependencies

Warning
Location
SKILL.md:246
Finding

Unpinned Third-Party Dependencies in CI/CD Workflow

Content
View full analysis
aws-secret-access-key: $ - name: Terraform initialization run: cd infra && terraform init - name: Compliance check run: | pip install checkov checkov -d infra/ --framework terraform - name: Terraform plan run: cd infra && terraform plan -out=tfplan - name: Upload plan uses: actions/upload-artifact@v3 with: name: tfplan path: infra/tfplan apply: name: Terraform apply needs: plan if: github.ref == 'refs/heads/main' && github.event_name == 'push' runs-on: ubuntu-latest environment: production steps: - uses: actions/checkout@v3 - name: Download plan uses: actions/download-artifact@v3 ``` ### Technical Analysis The workflow example installs `checkov` without an exact version or package hash. Consequently, every CI execution may resolve a different package release and a different set of transitive dependencies. If an upstream package, maintainer account, or package registry entry is compromised, attacker-controlled code could execute during installation or subsequent invocation. The workflow also references GitHub Actions through mutable major-version tags such as `@v2` and `@v3`. These references do not cryptographically bind execution to the code reviewed when the workflow was created. A compromised upstream action repository or modified tag could therefore change the code executed by future workflow runs. This risk is particularly important because the workflow configures AWS credentials before installing and running the unpinned dependency. Depending on runner and credential isolation, a compromised co ...[truncated 1696 chars]
Remediation
View remediation
' ``` 2. Use a lock file with cryptographic hashes, such as a `requirements.txt` generated by `pip-compile`: ```text checkov== \ --hash=sha256: ``` Install it with hash enforcement: ```bash python -m pip install --require-hashes -r requirements.txt ``` 3. Pin every GitHub Action to a reviewed full commit SHA rather than a mutable major-version tag: ```yaml - uses: actions/checkout@ - uses: aws-actions/configure-aws-credentials@ - uses: actions/upload-artifact@ - uses: actions/download-artifact@ ``` 4. Add comments identifying the corresponding action release version so pinned commits remain maintainable. 5. Use automated dependency update tooling, but require human review and CI validation before accepting updated package versions or action SHAs. 6. Restrict the workflow token and cloud identity to the minimum permissions required. Use short-lived, OpenID Connect-based cloud credentials instead of long-lived access keys. 7. Separate compliance scanning and Terraform planning from deployment credentials. Do not expose production cloud credentials to dependency-installation steps unless strictly necessary. 8. Restrict outbound network access where supported, retain protected production environments, and require manual approval before applying infrastructure changes. 9. Verify package provenance, review dependency changes, and use trusted internal mirrors or registries for security-sensitive CI dependencies. ]]>
Vulnerability Patterns
  • Excessive AgencyUnrestricted Tool Access, Autonomous Decision Making, Scope Creep
  • Trigger AbuseOverly Broad Trigger, Shadow Command Trigger, Keyword Baiting Trigger
  • MCP Tool PoisoningHidden Instructions, Unicode Deception, Parameter Description Injection
  • Prompt InjectionInstruction Override, Hidden Instructions, Exfiltration Commands
  • Data ExfiltrationExternal Transmission, Env Variable Harvesting, File System Enumeration
Findings (7)

Context-Inappropriate Capability

High
Category
Not specified by scanner
Confidence
98% confidence
Finding

The skill includes automatic deployment and configuration execution, including Terraform apply and Ansible runs, despite being presented as a security/compliance tool. In this context, hidden or unexpected write/execute behavior can lead to unauthorized infrastructure changes, privilege misuse, production outages, or costly resource creation if invoked under a more trusted security-analysis pretext.

Content

No source excerpt is available for this finding.

Description-Behavior Mismatch

Medium
Category
Not specified by scanner
Confidence
97% confidence
Finding

The manifest positions the skill as being for security detection, compliance audit, vulnerability scanning, and encryption protection, but the body implements broad multi-cloud provisioning, CI/CD deployment, configuration management, and disaster recovery workflows. This scope mismatch is dangerous because users or policy systems may grant trust, permissions, or invocation based on a narrower security-focused description while the skill can actually perform high-impact operational changes.

Content

No source excerpt is available for this finding.

Vague Triggers

Medium
Category
Not specified by scanner
Confidence
93% confidence
Finding

The trigger example is broad and overlaps with common user requests such as initializing multi-cloud infrastructure, increasing the chance of accidental invocation. Because this skill has powerful file, shell, and infrastructure-automation semantics, a generic trigger can cause high-impact operational behavior to be selected in contexts where the user did not intend to authorize it.

Content

No source excerpt is available for this finding.

Autonomous Decision Making

Medium
Category
Excessive Agency
Confidence
99% confidence
Finding

The CI/CD workflow uses terraform apply -auto-approve, enabling autonomous state-changing infrastructure execution without an interactive approval checkpoint at execution time. In a skill already mis-scoped as security/compliance oriented, this is especially dangerous because accidental or malicious invocation could push unchecked changes into production rapidly, causing outages, drift, or unauthorized resource creation.

Content

Scanner excerpt · SKILL.md (reported line 279)May include surrounding context.

md
name: tfplan
          path: infra/
      - name: Terraform执行
        run: cd infra && terraform apply -auto-approve tfplan
      - name: Ansible配置
        run: ansible-playbook -i inventory playbook.yml
      - name: 健康检查

Context-Inappropriate Capability

Medium
Category
Not specified by scanner
Confidence
93% confidence
Finding

The disaster recovery section orchestrates backup, failover, DNS updates, and recovery actions that are unrelated to a narrowly described security-audit use case. This expands the operational blast radius substantially: if triggered unexpectedly, it could alter production routing, initiate failover, or change cross-region resources under the guise of a security-focused skill.

Content

No source excerpt is available for this finding.

Intent-Code Divergence

Medium
Category
Not specified by scanner
Confidence
96% confidence
Finding

The document claims the skill is pure Markdown and requires no extra installation, yet elsewhere directs use of cloud CLIs, Terraform, Ansible, and related runtime tooling. This inconsistency can mislead users and security reviewers about the execution model, dependencies, and risk surface, causing them to underestimate the need for sandboxing, permissions review, and tool hardening.

Content

No source excerpt is available for this finding.

Natural-Language Policy Violations

Low
Category
Not specified by scanner
Confidence
88% confidence
Finding

The summary and description are primarily written in Chinese and include mixed-language usage such as "Use when 需要安全检测...", but the document does not state that language selection is optional or user-driven. This can create a locale policy issue if the skill effectively assumes a specific language without opt-in.

Content

No source excerpt is available for this finding.

Static analysis

No suspicious patterns detected.