File Protection
Protect LLM model files against unauthorized access and modification. Use when designing, deploying, or reviewing storage and access controls for AI model files and weights.
Install
$ agentstack add skill-redhatproductsecurity-prodsec-skills-file-protection ✓ scanned · ✓ verified, works with Claude Code, Cursor, and more.
Security review
✓ PassedNo issues found. Passed automated security review. · v0.1.0 How review works →
- ✓ Prompt-injection patterns
- ✓ Secret / credential exfiltration
- ✓ Dangerous shell & filesystem operations
- ✓ Untrusted network calls
- ✓ Known-malicious package signatures
What it can access
- ✓ Network access No
- ✓ Filesystem access No
- ✓ Shell / process execution No
- ✓ Environment & secrets No
- ✓ Dynamic code execution No
From automated source analysis of v0.1.0. “Used” means the capability is present in the source — more access means more to trust, not that it’s unsafe.
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Passed review? Show it. Paste this badge into your README, it links to the public security report.
Links to your public security report.[](https://agentstack.voostack.com/security/report/skill-redhatproductsecurity-prodsec-skills-file-protection) Reliability & compatibility
Declared compatibility
Compatibility is declared by the source manifest. End-to-end runtime verification is coming, see below.
We're building live execution health for every listing: tool-call success rate, median latency, uptime, and last-checked timestamps, measured, not self-reported. It isn't live yet, so we don't show numbers we can't stand behind.
How agent discovery & health will work →About
LLM File Protection
Security Requirement
Large Language Models are composed of files (weights, configuration, tokenizer, etc.) loaded by the inference engine. These files MUST be protected against unauthorized access and modification.
Risks of Unprotected Model Files
| Risk | Impact | |---|---| | Unauthorized access | Model theft, intellectual property loss | | Unauthorized modification | Backdoored or poisoned model served to users | | Tampering | Modified weights producing biased, harmful, or incorrect outputs | | Data extraction | Extracting sensitive training data embedded in model weights |
Required Controls
| Control | Description | |---|---| | Access control | Only authorized users and services can read model files | | Write protection | Only authorized pipelines can modify model files | | Integrity verification | Detect unauthorized modifications via checksums or signatures | | Encryption at rest | Encrypt model files on disk to protect against storage-level access | | Audit logging | Log all access to model files |
Implementation Checklist
- [ ] Store model files in access-controlled storage (not world-readable)
- [ ] Grant read access only to the inference engine service account
- [ ] Grant write access only to authorized model deployment pipelines
- [ ] Mount model files as read-only in the inference engine container
- [ ] Enable encryption at rest for model storage volumes
- [ ] Implement integrity checks (checksums or signatures) verified at load time
- [ ] Log all access to model files for audit
- [ ] Regularly audit file permissions and access patterns
Source & license
This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.
- Author: RedHatProductSecurity
- Source: RedHatProductSecurity/prodsec-skills
- License: Apache-2.0
Install and usage instructions live in the source repository linked above.
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Versions
- v0.1.0 Imported from the upstream source.