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Assess the network protocols and services in use for security weaknesses — cleartext protocols, weak/outdated crypto and TLS, insecure or legacy services, and man-in-the-middle exposure. Use to review what's running on the network and how securely it communicates.
Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.
SOC 직업 분류 기준
| name | protocol-security-review |
| description | Assess the network protocols and services in use for security weaknesses — cleartext protocols, weak/outdated crypto and TLS, insecure or legacy services, and man-in-the-middle exposure. Use to review what's running on the network and how securely it communicates. |
A view of where network communications and services are weak — unencrypted, weakly encrypted, or inherently insecure — with concrete upgrades.
network-segmentation-review).network-pentest enumeration or
config review).A table: service/protocol · weakness · exposure · severity · secure replacement.
Confirmed issues → security-reporting:finding.
Cleartext credentials on the wire and name-resolution poisoning (LLMNR/NBT-NS) are classic, high-impact, and common — prioritize them. Disabling legacy protocols (SMBv1, SSLv3/TLS1.0) is usually a high-value, low-friction win.
Statically review a Claude Code / AI-agent setup for security misconfigurations — risky hooks, over-broad permissions, untrusted or cleartext MCP servers, hardcoded secrets, endpoint redirection, over-privileged agents/skills, and prompt-injection in steering files. Use when asked to audit a `.claude/` directory, `settings.json`, `.mcp.json`, a plugin/marketplace, or `CLAUDE.md`, or to add a config-security gate to CI. Drives the `agentscanner` CLI.
Review the training / MLOps pipeline for security weaknesses — data-poisoning surface, feature-store and data-source trust, experiment-tracking and model-registry access control, secrets, and reproducibility/provenance. Use when assessing how models are built and promoted, not how they're served or consumed.
Review the provenance and integrity of models, datasets, and ML artifacts pulled from hubs/registries — unsafe deserialization (pickle, PyTorch/Keras/joblib), untrusted model sources, model/dataset tampering, and signing. Use when an app loads third-party model weights or datasets, or before promoting a model.
Harden a deployed model inference endpoint — authn/authz, rate limiting and abuse control, input validation, and exposure to model-extraction / inversion / membership-inference attacks. Use when reviewing how a model is served (REST/gRPC, Triton/TorchServe/KServe, or a hosted inference API), not how it was trained.
Export a finished security artifact (report, executive summary, diagram, spreadsheet of findings) to Google Drive — right folder, clear naming, and shareable with the intended audience. Use when a deliverable needs to be handed to stakeholders who live in Drive/Docs/Sheets rather than a wiki or tracker.
Turn a security finding (vuln, pentest issue, review item) into a well-formed Jira issue — mapped severity→priority, remediation/repro in the body, labels and components set, and dedup-checked against existing issues. Use when findings need to become tracked, assignable work in Jira.