discover-attack-surface

A security-analysis step that identifies project dependencies and records methods where untrusted data first enters the code.

In plain words
What is it for?
Use it during an OpenTaint source-discovery pass for a specified language, project, and analysis plan.
Why use it?
It fills gaps in built-in security rules so later analysis can track potentially unsafe data flows more completely.

Skill for Claude CodeCodex

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add skills/seqra/opentaint/discover-attack-surface
Any agent
npx skills add seqra/opentaint --skill discover-attack-surface
Clone the repo
git clone --depth 1 https://github.com/seqra/opentaint

Made for: Claude Code, Codex.

Per session 36 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,643 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5 $0.00036 $0.01643
Opus 5 $0.00018 $0.00822
Sonnet 5 $0.00007 $0.00329
Haiku 4.5 $0.00004 $0.00164

Measured yesterday against content hash 639698ea3091, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

discover-attack-surface scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured yesterday.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

skills/discover-attack-surface/SKILL.md · 98 lines

How it starts

The opening of the file, as written. The whole thing — 98 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Skill: Discover Attack Surface

Work one assignment of project-used dependency members and pick out the taint sources among them — the methods where untrusted data first enters. The concrete inspection commands and value formats are language-specific — read references/<language>.md per Inputs and follow its numbered steps, which key to the ones below

Inputs

Provided by the caller, fall back to the default value when omitted. Ask back only when a required input is missing and has no sensible default

  • project-root (optional) — root of the target project. Opentaint keeps all analysis artifacts under the fixed <project-root>/.opentaint/ directory, so every .opentaint/... path below resolves there. Default: current directory
  • language (required) — target language for this project and language-specific instructions
  • plan (required) — path to this agent's partition plan .opentaint/tracking/rules/plans/<id>.yaml: the project-used members to classify

Workflow

1. Settle built-in coverage first

Before anything, for each package the plan touches see what the built-in source rules already match for its members — opentaint health --rules prints the built-in rules root path; browse it and the project's own rules (per the language reference). This decides whether you write a source unit:

  • full — existing rules already match the project-used sources → write no unit, stop, don't drill further
  • partial — some project-used sources matched, others missed → plan only the missing used members
  • none — plan the package's project-used sources from scratch

2. Classify the plan's members

The members are the FQNs under the plan's scopes — the project-used scope, already extracted, and only the members not yet classified in a prior run. Confirm each package's dependency identity and inspect its signatures/docs while classifying (per the language reference); read app source, dependency API/docs, and framework config to classify the listed members.

Read the full file on GitHub · 98 lines

Files

What ships with it

1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. yesterday First seen · 98 lines · 36 tokens per session scan A 639698ea3091

Subscribe to this mod's changes

discover-attack-surface is a skill published in the GitHub repository seqra/opentaint (149 stars, last pushed 2d ago), licensed Apache-2.0. It adds 36 tokens to every session and 1,643 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

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