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Reporting Security Findings

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trilwu
reporting-security-findings

Write security findings and assessment reports — severity scoring with CVSS and business impact, reproducible proof of concept, remediation guidance, executive summaries, and coordinated disclosure. Use when writing up a vulnerability, producing a pentest or audit report, triaging a bug bounty submission, or preparing a disclosure timeline.

Overview

Publishertrilwu
Repositorysecskills
Skill namereporting-security-findings
Stars
144
Forks
15
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 trilwu on GitHub. Read the source before you install it.

Installation

Install the Reporting Security Findings 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.
  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/trilwu/secskills.git /tmp/secskills
mkdir -p .claude/skills
cp -r /tmp/secskills/secskills-core/skills/reporting-security-findings .claude/skills/reporting-security-findings
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Reporting Security Findings 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 Reporting Security Findings 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 Reporting Security Findings 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.

Reporting Security Findings

The report is the product. Findings that are not understood are not fixed, and findings that cannot be reproduced are disputed. Most of the value an assessment creates is destroyed or preserved in the write-up.

When to Use

  • Writing up a single vulnerability
  • Producing a penetration test, code audit, or red team report
  • Submitting to a bug bounty program or triaging inbound submissions
  • Scoring severity and arguing priority with an engineering team
  • Planning coordinated disclosure for an unpatched issue

When NOT to Use

  • Finding the bug — use the relevant testing or audit skill first
  • Assembling the raw record a report is built from (provenance, artifact register, evidence, dead ends) — use maintaining-engagement-state; this skill consumes that record
  • Incident narratives and postmortems — use responding-to-incidents
  • Compliance-framework mapping as the primary goal — different document, different audience

Anatomy of a Finding

Every finding answers five questions, in this order:

markdown
### F-03  Tenant isolation bypass in report export     [High]

**Summary**
An authenticated user of any tenant can export reports belonging to other
tenants by supplying an arbitrary report ID to `GET /api/v2/reports/{id}/export`.

**Impact**
Full read access to other customers' report data, including the financial
figures and contact records those reports contain. Any customer account —
including a self-service trial — is sufficient. This is a cross-tenant
confidentiality breach with likely contractual and regulatory consequences.

**Affected**
`api/handlers/reports.go:214` (`handleExport`), deployed in production as of
commit `a1b2c3d`. Reproduced on staging 2026-07-24 14:02 UTC.

**Reproduction**
1. Authenticate as `trial-user@tenant-a` and obtain a session token.
2. Note your own report ID from `GET /api/v2/reports` (e.g. `1041`).
3. Request a neighbouring ID:
   curl -H "Authorization: Bearer $TOKEN" \
        https://staging.example.com/api/v2/reports/1042/export -o out.csv
4. `out.csv` contains tenant B's data. Confirmed with IDs 1042, 1043, 1055.

**Root cause**
The handler looks the report up by primary key and checks only that the
session is valid. The tenant scope present on the list endpoint
(`WHERE tenant_id = ?`) is absent from the export query.

**Remediation**
Add the tenant predicate to the export lookup, and enforce it at the data
access layer rather than per handler so new endpoints inherit it:
    SELECT ... FROM reports WHERE id = ? AND tenant_id = ?
Then audit the remaining 14 handlers that call `findByID` without a scope —
listed in Appendix B.

**References**
CWE-639, OWASP API1:2023 Broken Object Level Authorization

Rules that make findings act-on-able:

  • One finding per issue. Bundling ten IDORs into "authorization issues" guarantees partial fixes.
  • Exact locations. File, line, endpoint, commit, and the environment where you reproduced it.
  • Reproduction someone else can run without asking you a question. Include the setup, the request, and the observed result — not just the payload.
  • Root cause, not just symptom. The fix for a symptom leaves the class.
  • Remediation that names the change. "Validate input" is not remediation.
  • Variants listed. If you found one instance and suspect more, say what you checked and what you did not.
  • Revalidated against what ships. Before a finding goes in the report, confirm the vulnerable code is not already fixed on a branch you haven't pulled (git log --oneline <checkout>..origin/main -- <file>). One already-patched finding teaches the reader to distrust the rest.

Invariants to check before a finding leaves draft

Four of the rules above are mechanical enough to check rather than judge. Run them over every finding; each maps to a way reports have actually gone wrong.

  1. Every finding cites at least one piece of evidence — a command and its output, a request and response, a crash, a screenshot. A finding whose only support is a code reading is a candidate, not a finding. Label it so.
  2. Confirmed status and low confidence cannot coexist. If you would not bet on it, it is still a candidate. Downgrade the status or say plainly what would settle it. This pair is the single most common way a speculative finding acquires unearned authority on its way into a report.
  3. Every reproduction runs without asking you a question, or names the environment it cannot leave — an offline sample, a lab-only target, a credential the reader must supply. An unreproducible finding is not a finding you can defend in a remediation meeting.
  4. A claim of obtained access or data has evidence of that specific claim. "Full database read" needs a row you actually read, not an injectable parameter plus an inference about what lies behind it. Reachability is not impact.

When a finding fails one of these, it does not get quietly dropped: it moves to the candidate worklist with the reason attached, so a later pass knows what would promote it. See orchestrating-vulnerability-research for how candidates are graded when a separate critic does the promoting.

Severity

CVSS is the common currency, but it scores a vulnerability in the abstract. Score with CVSS, then state business impact separately — engineering prioritizes on the second.

CVSS 4.0 base vector example:
CVSS:4.0/AV:N/AC:L/AT:N/PR:L/UI:N/VC:H/VI:N/VA:N/SC:N/SI:N/SA:N   → 6.9 Medium

Then adjust for reality and say why:

FactorRaises priorityLowers priority
ExposureInternet-facing, unauthenticatedInternal only, requires admin
DataRegulated, customer, credentialsSynthetic, public
Exploit maturityPublic exploit, active exploitationTheoretical, complex chain
Compensating controlsNoneWAF rule, network segmentation, monitoring
Blast radiusCross-tenant, whole fleetSingle account, single record

A Medium CVSS that breaks tenant isolation for a SaaS product is a P1 regardless of the number. Say that explicitly rather than letting the score argue for you. Conversely, do not inflate scores: a report where everything is Critical gets triaged by ignoring it.

Chained findings: report the components individually and report the chain as its own finding with the chain's severity. The chain is what an attacker does; the components are what engineering fixes.

Proof of Concept

Scale the PoC to what proves the point:

  • Enough to prove control of the sink. id=1042 returning another tenant's row proves the bug. Dumping the full database does not prove it harder.
  • Non-destructive by default. Do not modify or delete data to demonstrate write access; write a benign marker to a record you own, or demonstrate the authorization decision without the effect.
  • Redact real data. If your evidence contains customer records, redact them in the report and store the raw evidence separately with access controls.
  • State what you did. If you created accounts, uploaded files, or left artifacts, list them so they can be cleaned up.

For memory-safety and exploitation findings, a crash with a controlled instruction pointer plus an analysis of exploitability is usually the right depth. A weaponized exploit belongs in the report only when the engagement explicitly calls for it.

The Report

1. Executive summary        — 1 page, no jargon, answers "how bad and what now"
2. Scope and methodology    — what was tested, how, and with what access
3. Coverage and limitations — what was NOT tested, and why
4. Findings                 — ordered by severity, each self-contained
5. Strategic recommendations— themes across findings, not per-finding fixes
6. Appendices               — tooling, raw output, evidence index, retest results

The executive summary is written for someone who will read only it. Three things: the overall risk position in a sentence, the two or three findings that matter, and what decision is being asked for. No CVSS vectors, no tool names, no "we ran Nessus."

Coverage and limitations is the section that protects everyone. State the time box, the accounts and environments you had, the components you could not reach, and the testing you were asked not to do. A report silent on limitations implies coverage it did not have, and that silence is what turns a missed bug into a dispute.

Strategic recommendations are where an assessment earns repeat work: the themes. "Authorization is enforced per handler rather than at the data layer; 9 of 14 findings share this root cause." That sentence is worth more than the nine findings.

Writing for the Audience

ReaderWantsGive them
ExecutiveRisk and decisionOne page, plain language, business consequence
Engineering managerPrioritization and effortSeverity, root cause, scope of the fix
EngineerTo fix it todayExact location, reproduction, concrete change
Compliance/auditEvidence and mappingMethodology, coverage statement, framework refs

Write the finding for the engineer, and the summary for the executive. Do not average the two into prose that serves neither.

Tone: describe the defect, not the developer. "The export handler omits the tenant predicate" — not "the developer forgot." Reports circulate, and an accusatory report makes the next engagement harder.

Disclosure

For findings in software you do not own:

Day 0     Report privately: security.txt, /security, GitHub advisory, CERT
Day 0-7   Acknowledge receipt; agree a timeline
Day 45    Check in; offer help reproducing
Day 90    Standard public disclosure deadline (adjust for severity and
          exploitation in the wild — actively exploited issues warrant faster
          public warning; complex fixes may warrant an extension you agree to)
  • Give a specific deadline at first contact, and honour it.
  • Request a CVE when the issue affects released software with other users.
  • Do not publish exploit code before a fix is broadly available; describe impact and mitigation instead.
  • If the vendor is unresponsive, escalate to a CERT/CSIRT coordinator rather than going straight to publication.
  • Never test beyond the authorized scope to "improve the report," and never use a finding as leverage. Both convert a research contribution into a legal problem.

For bug bounty submissions, read the program's scope and rules first, report one issue per submission, and include the impact statement the triager needs to justify the payout internally.

Rationalizations to Reject

  • "They'll understand what I mean." They will not, and they will not ask — they will downgrade it.
  • "I'll write it up later." Reproduction details decay within hours.
  • "It's obviously Critical." Then it is easy to justify. Justify it.
  • "Everything is High so they take it seriously." Inflated severity is how a report stops being read.
  • "I couldn't fully exploit it, so I'll leave it out." Report it with the evidence you have and state the uncertainty. Silent omission is worse.
  • "I found it in the code, so it's still there." You may be reading a stale checkout. Revalidate against what ships before you file it — a finding fixed three commits ago discredits the whole report.
  • "The client won't like the limitations section." They will like it less after a breach in an area the report implied was covered.
  • "No findings means a bad report." A report with honest coverage and no findings is a valid result. Say what you tested and how deeply.

Reading External Sources

Fetch public advisories, specifications, and vendor reports as Markdown:

bash
curl -sL "https://defuddle.md/<url>"      # scheme in the path is optional

This strips page boilerplate — roughly 78% fewer tokens on a prose page — and returns the full text rather than a summary, so you can grep it and trust a negative result.

Three things it is not for. Fetch JSON and API responses raw, because readability extraction mangles structured data. Fetch authenticated or JavaScript-rendered pages directly, because it retrieves them anonymously. And never route adversary infrastructure (phishing links, C2, malware hosting), client-owned hosts, or engagement URLs through it — the request leaves your machine to a third party, and for live adversary infrastructure it also tips off the operator.

Some sites block the extractor and return an error blob rather than the page — {"error":"Failed to fetch: 418 I'm a teapot"} from freedesktop.org, for instance. That is the fetch being refused, not the source saying the thing does not exist. Re-fetch the URL directly before drawing any conclusion from it.

References

  • auditing-code-for-vulnerabilities — the audit-side deliverable format
  • responding-to-incidents — incident narratives and postmortems
  • CVSS 4.0 specification; EPSS for exploitation likelihood; CWE for classification
  • ISO/IEC 29147 (vulnerability disclosure) and 30111 (handling)

Frequently asked questions

What does the Reporting Security Findings AI skill do?

Write security findings and assessment reports — severity scoring with CVSS and business impact, reproducible proof of concept, remediation guidance, executive summaries, and coordinated disclosure. Use when writing up a vulnerability, producing a pentest or audit report, triaging a bug bounty submission, or preparing a disclosure timeline.

Why use Reporting Security Findings on TypingMind?

Because you install it once and use it with any model. Reporting Security Findings 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 Reporting Security Findings in TypingMind?

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

Which AI models can use Reporting Security Findings?

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 Reporting Security Findings?

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

Is the Reporting Security Findings AI skill free?

Yes. It is published on GitHub by trilwu 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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