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Security Assessment

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amd
security-assessment

Assess a reported security vulnerability in GAIA and fill a PSIRT / JIRA triage: decide if it is valid & exploitable, whether it needs a CVE + bulletin, and produce the CVSS 4.0 score, CWE, and CVE description. Use when triaging a researcher report, a security advisory, or a claude-security-audit finding — anytime you must answer 'is this a CVE?' or produce a CVSS score/vector/severity. Always COMPUTES the score from a reviewed vector with util/cvss4.py (matches the FIRST 4.0 calculator); never guess the number — an LLM's guessed CVSS number is untrustworthy.

Overview

Publisheramd
Repositorygaia
Skill namesecurity-assessment
Stars
1.6K
Forks
176
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by amd on GitHub. Read the source before you install it.

Installation

Install the Security Assessment AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/amd/gaia/tree/main/.claude/skills/security-assessment
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/amd/gaia.git /tmp/gaia
mkdir -p .claude/skills
cp -r /tmp/gaia/.claude/skills/security-assessment .claude/skills/security-assessment
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Security Assessment in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Security Assessment on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Security Assessment is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Security Assessment (PSIRT / CVSS triage)

For scoring a reported GAIA vulnerability and filling the PSIRT triage template. The one hard rule: the CVSS number is arithmetic on a reviewed vector, never a guess.

Rule 0 — never guess a CVSS score. Compute it.

The vector is the human judgment call. The number is math. Pick the vector, then run:

bash
python util/cvss4.py "CVSS:4.0/AV:L/AC:L/AT:N/PR:L/UI:A/VC:L/VI:H/VA:H/SC:N/SI:N/SA:N"
# -> {"vector": "...", "base_score": 5.3, "severity": "Medium"}

util/cvss4.py is a thin, tested wrapper over the cvss pip package (in the [dev] extra) whose 4.0 output matches https://www.first.org/cvss/calculator/4.0 exactly (anchored in tests/unit/test_cvss4.py). A malformed vector is a loud ValueError, never a silent 0.0.

Why this rule exists (real GAIA cases):

  • An AI triage on the find -exec ticket asserted "CVSS 6.9 Medium" for the vector CVSS:4.0/AV:L/AC:L/AT:N/PR:L/UI:N/VC:H/VI:H/VA:N/SC:N/SI:N/SA:N — which actually computes to 8.4 High.
  • The security-audit workflow's prior anchor: a triage guessed 7.3 for a vector that scores 8.7.

Guessed numbers are off by a full severity band. Always run the tool and quote its output.

The GAIA vector rubric (which metric to pick)

This mirrors the rubric baked into .github/workflows/claude-security-audit.yml (keep the two consistent). Full form: CVSS:4.0/AV:_/AC:_/AT:_/PR:_/UI:_/VC:_/VI:_/VA:_/SC:_/SI:_/SA:_.

  • AV — Network only if untrusted data crosses a network boundary to reach the sink (hub artifact, HTTP request). A local CLI/file input on the same host is Local.
  • AC — Low unless a real, specific condition (not just "attacker must try") is needed.
  • AT — Present if exploitation needs a precondition outside the attacker's control (a specific agent installed, victim points at a non-default hub); else None.
  • PR — None unless GAIA privilege/auth is required to trigger it.
  • UI — This is where GAIA agent-tool findings usually turn. Active if the user must click through a warning or approve a confirmation-gated tool (TOOLS_REQUIRING_CONFIRMATION); Passive if they must merely initiate a command/install; None only if fully automatic. Most run_shell_command / write-tool findings are UI:A because the operator sees and approves the literal command first.
  • VC disclosure/reads · VI writes/tampering · VA crash/DoS. Set the subsequent scope metrics (SC/SI/SA) only if the impact escapes into a separate security scope (VM, container, downstream client) — an in-process capability gain does not.

Incremental-impact check: score the capability the finding adds, not what the tool could already do. The find -exec bypass's novel gain is write/delete/exec (VI/VA); file reads were already reachable via other whitelisted commands under the same path controls, so its VC is Low, not High. Overstating VC:H is what pushed the AI vector to a High score for a Medium-in-practice issue.

The confirmation gate changes the answer to "is this a CVE?"

Many GAIA agent tools are gated behind explicit per-command user approval (TOOLS_REQUIRING_CONFIRMATION in src/gaia/agents/base/agent.py). When the user sees and approves the literal command before it runs, that human-approval step is the real security boundary. A bypass of a secondary control (e.g. an incomplete command allowlist) layered on top of an intact primary control is defense-in-depth hardening, not a standalone exploitable vulnerability — it typically does not warrant a CVE + public bulletin.

Check before deciding:

  • Is the tool in TOOLS_REQUIRING_CONFIRMATION? (grep it.)
  • Does it get denied in non-interactive mode, or only run under an opt-in like GAIA_AUTO_APPROVE_TOOLS=1? The opt-in is a documented, user-accepted risk.
  • Is there a code path (API server, programmatic SDK) that skips the gate? If yes, the gate is not universal and the CVE bar may be met after all — verify, don't assume.

Fix it regardless (defense-in-depth), but let the gate drive the CVE decision.

CWE — name the root cause, not the impact

Rank the root cause first, the consequence second. For the find -exec bypass: CWE-184 (Incomplete List of Disallowed Inputs) is the root cause (the allowlist check is incomplete); CWE-78 (OS Command Injection) is only the consequence. An AI triage that leads with CWE-78 has described the symptom, not the defect.

Filling the PSIRT / JIRA triage

Answer in this order (the template PSIRT sends):

  1. Valid, exploitable vulnerability? Yes/No — be honest about the bypass even if a gate limits it; qualify exploitability rather than denying the weakness.
  2. Needs a CVE ID + public Security Bulletin? Yes/No — apply the confirmation-gate test above. If No, state whether a security brief is required (usually optional).
  3. If new CVE: CVSS 4.0 (Score + Vector — from util/cvss4.py, not guessed), CWE (root-cause first), and CVE description in the required shape: <Weakness> in <component> could allow <attacker> to <exploit> potentially resulting in <CIA>.
  4. Mitigation delivery / AMD deliverables — for a GAIA code fix this is the amd-gaia package (PyPI + GitHub) only; None for PI / SEV FW / uCode / ROCm / Radeon / Adrenalin / uProf / Chipset drivers. Not a Linux-upstream issue.

Then move the JIRA ticket Opened → Assessed.

Worked example — the find -exec triage (2026-07)

Vectorutil/cvss4.py saysNote
CVSS:4.0/AV:L/AC:L/AT:N/PR:L/UI:N/VC:H/VI:H/VA:N/SC:N/SI:N/SA:N8.4 Highthe AI's vector — it claimed 6.9
CVSS:4.0/AV:L/AC:L/AT:N/PR:L/UI:A/VC:L/VI:H/VA:H/SC:N/SI:N/SA:N5.3 Mediumcorrected: UI:A (approval gate), VC:L (reads already possible), VA:H (-delete)

Both rows are complete vectors — paste either straight into util/cvss4.py.

Outcome: valid weakness, No CVE (confirmation gate is the real boundary), fixed as hardening in PR #2740. The corrected vector — computed, not guessed — lands a full band below the AI's assertion.

Writing it up

The triage you hand back follows CLAUDE.md → How You Communicate: open with the verdict in plain words — is it real, is it exploitable, does it need a CVE — then the CVSS vector, CWE, and evidence underneath. A reviewer deciding whether to file should not have to parse the vector string to learn the answer. State the computed score, never a guessed one.

Frequently asked questions

What does the Security Assessment AI skill do?

Assess a reported security vulnerability in GAIA and fill a PSIRT / JIRA triage: decide if it is valid & exploitable, whether it needs a CVE + bulletin, and produce the CVSS 4.0 score, CWE, and CVE description. Use when triaging a researcher report, a security advisory, or a claude-security-audit finding — anytime you must answer 'is this a CVE?' or produce a CVSS score/vector/severity. Always COMPUTES the score from a reviewed vector with util/cvss4.py (matches the FIRST 4.0 calculator); never guess the number — an LLM's guessed CVSS number is untrustworthy.

Why use Security Assessment on TypingMind?

Because you install it once and use it with any model. Security Assessment is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Security Assessment in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/amd/gaia/tree/main/.claude/skills/security-assessment. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Security Assessment?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Security Assessment?

As many as you like. As long as a model supports skills, you can use Security Assessment with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Security Assessment AI skill free?

Yes. It is published on GitHub by amd under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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