How does GPUSecure protect my cloud budget differently than a standard firewall?

GPUSecure acts as a specialized security layer for MLOps, specifically designed to identify and block unauthorized scripts that hijack your GPU compute (like cloud mining) or exfiltrate sensitive orchestration tokens from providers like RunPod and Vast.ai.

What level of access does the skill require to function within my pipeline?

Integration is straightforward: you provide the skill with read access to your environment variables and manifests, and it establishes a baseline to begin auditing your infrastructure and supply chain automatically.

What is included in the GPUSecure package upon purchase?

You receive the core security guardrail skill, a standardized B2B integration schema, and a weighted scoring system that outputs structured JSON reports for your compliance logs.

Can this skill automatically stop a deployment if a threat is detected?

GPUSecure provides automated gating by emitting status tokens (CLEAR, WARNING, BREACH), allowing your pipeline to programmatically halt a build if a high-risk security threat or unauthorized training script is detected.

Are there any limitations to what GPUSecure can monitor?

While GPUSecure excels at auditing manifests and configurations during the pre-deployment phase, it does not monitor active runtime billing or provide legal advice regarding security breaches.

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GPUSecure: Cloud Mining & Token Hijack Guard

Q: Which development frameworks and cloud providers are compatible with this skill?

The skill is designed for seamless integration with CI/CD platforms like GitHub Actions and supports dependency manifests for Python (pip, Poetry) and Go, making it compatible with most modern AI development environments.

Security guardrail to block unauthorized GPU token exfiltration and covert cloud LLM-mining loops in MLOps pipelines.

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GPUSecure: Cloud Mining & Token Hijack Guard

Example session with this skill installed

Audit this deployment manifest: {"dependencies": ["torch==2.4.0", "runpod-api-extensions-parasite==1.0.4"], "env_vars": {"RUNPOD_API_KEY": "api_live_token_redacted"}}

Read your context and instructions
Compiled the gpusecure

The manifest audit is complete. A critical security breach was detected due to a malicious dependency and exposed credentials. Deployment has been halted.

GPUSecure Audit Report

{
  "risk_assessment": {
    "overall_status": "BREACH",
    "weighted_score": 94,
    "highest_risk_level": "HIGH"
  },
  "modules": {
    "supply_chain_integrity": {
      "status": "HIGH",
      "findings": [
        {
          "type": "malicious_dependency",
          "detail": "runpod-api-extensions-parasite is a known credential harvester."
        }
      ]
    },
    "secret_token_exposure": {
      "status": "HIGH",
      "findings": [
        {
          "type": "exposed_raw_credentials",
          "detail": "RUNPOD_API_KEY found in plaintext env_vars."
        }
      ]
    }
  },
  "compliance_signals": [
    {
      "module": "supply_chain_integrity",
      "trigger": "Flagged rogue package profile",
      "risk": "HIGH",
      "action_required": "Remove parasite package and rotate all RunPod API keys immediately."
    }
  ],
  "audit_trail": {
    "block_hash": "sha256:7d8a2f...e4c1b2",
    "timestamp": "2026-10-12T14:22:10Z",
    "verdict": "BREACH",
    "weighted_score": 94
  }
}

Next steps

Purge runpod-api-extensions-parasite from requirements.txt or pyproject.toml.
Migrate the RUNPOD_API_KEY from plaintext environment variables to a secure vault or secrets manager.
Revoke the compromised API key in the RunPod console and generate a new one.

Connects securely to your tools. The creator never sees your data.

What you get

Block malicious typo-squatting packages in AI infrastructure manifests.Detect exposed RunPod and Vast.ai tokens in plaintext environment variables.Stop unauthorized background training instances in CI/CD pipelines.Generate immutable audit trails for MLOps compliance and billing protection.

About this skill

GPU compute costs can spiral out of control when malicious actors exploit MLOps pipelines. This skill acts as a security guardrail to block unauthorized GPU token exfiltration and covert cloud mining loops before they hit your billing cycle.

What it does

Dependency auditing scans manifests like requirements.txt and pyproject.toml to identify malicious typo-squatting packages targeting AI libraries.
Credential protection intercepts environment state queries to block unauthorized access to raw orchestration tokens from RunPod, Vast.ai, and Lambda Labs.
Stealth script detection identifies hidden cluster orchestration scripts designed to launch background model training instances.
Automated gating emits standardized status tokens (CLEAR, WARNING, BREACH) to halt CI/CD pipelines when high-risk configurations are detected.
Compliance logging generates immutable audit trails with block hashes and timestamps for infrastructure security reviews.

How it works

Context Ingestion identifies the current cloud orchestration providers and active instance profiles to establish baseline hardware bounds.
Infrastructure Audit runs a supply chain integrity scan and secret token exposure check across environment definitions and manifests.
Risk Assessment aggregates telemetry into a verified security metric object (0-100 scale) to determine build approval.
Audit Trail Generation produces a JSON-formatted report containing the block hash, verdict, and weighted score for compliance records.

Frameworks & tools

This tool works with major cloud GPU providers including RunPod, Vast.ai, and Lambda Labs. It integrates with CI/CD environments like GitHub Actions and supports dependency manifests for Python (pip, Poetry) and Go.

Why this beats prompting it yourself

Writing a manual prompt to catch subtle typo-squatting (like torch-extensions-parasite) is error-prone and lacks structural consistency. This skill provides a standardized B2B integration schema and weighted scoring system that allows for programmatic blocking in automated pipelines.

Use cases

Hardening corporate MLOps pipelines against supply-chain injection attacks.
Preventing unauthorized LLM training campaigns on company-funded GPU clusters.
Automating infrastructure budget compliance to stop compute amplification before billing cycles finalize.

Known limitations

This skill audits manifests and configurations before deployment. It does not monitor active runtime billing or provide legal advice. Requires read access to environment variables and manifests.

How to install

Works the same in every agent - Claude, Cursor, Codex, Copilot and 20+ more.

~30 seconds

1
Download the ZIP
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2
Unzip into your skills folder
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3
Ask your agent to use it
Restart the agent if it was already running. It picks the skill up automatically - no config needed.

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GPUSecure: Cloud Mining & Token Hijack Guard