attack-path-analysis

attack-path-analysis is a skill for Codex from vlinx-io/VelaTerm. It costs 60 tokens per session (1,608 once invoked), scanned A, original, MIT.

A security-analysis step for tracing a suspected weakness from its starting input to the place where it causes harm. It turns that path into an attacker story and decides how serious and reportable the finding is.

In plain words
What is it for?
Use it after a security scan has identified a finding, or when explicitly asked to trace a source-to-sink path, assess exploitability, and produce a security report decision.
Why use it?
It helps distinguish a real exploitable issue from a merely possible one and calibrates severity against the stated threat model. It is not a general scan of a repository, branch, commit, or pull request.

Skill for Codex

Written for Codex: agents/openai.yaml present. Also seen: mentions Codex; $skill-name invocation.

Good fit Use it after a security scan has identified a finding, or when explicitly asked to trace a source-to-sink path, assess exploitability, and produce a security report decision.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/vlinx-io/velaterm/attack-path-analysis
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.

Any agent
npx skills add vlinx-io/VelaTerm --skill attack-path-analysis
Clone the repo
git clone --depth 1 https://github.com/vlinx-io/VelaTerm

Made for: Codex.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for attack-path-analysis

README.md
[![agentmods](https://agentmods.dev/badge/skills/vlinx-io/velaterm/attack-path-analysis/github.svg)](https://agentmods.dev/skills/vlinx-io/velaterm/attack-path-analysis)
Your own site
<a href="https://agentmods.dev/skills/vlinx-io/velaterm/attack-path-analysis"><img src="https://agentmods.dev/badge/skills/vlinx-io/velaterm/attack-path-analysis/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for attack-path-analysis

Your own site · 80×15
<a href="https://agentmods.dev/skills/vlinx-io/velaterm/attack-path-analysis"><img src="https://agentmods.dev/badge/skills/vlinx-io/velaterm/attack-path-analysis.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 60 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,608 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
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 5.1 $0.00060 $0.01608
Opus 5.5 $0.00024 $0.00643
Sonnet 5.5 $0.00012 $0.00322
Haiku 4.5 $0.00006 $0.00161

Measured 1mo ago against content hash 63e820ce598d, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

attack-path-analysis 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 1mo ago.

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.

Origin

Copies of this mod

1 near-identical copy found in the catalogue:

src-tauri/resources/codex-security/skills/attack-path-analysis/SKILL.md · 115 lines

How it starts

The opening of the file, as written. The whole thing — 115 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Security Attack Path Analysis

Objective

Turn validated or still-plausible findings into explicit attacker stories, structured attack-path analysis facts, severity calibration, and a final reportability decision grounded in the threat model.

Artifact Resolution

The path references in this skill are the default locations for this phase. If the user explicitly provides a different path for a required input or output, use the user-provided path instead of the corresponding default path referenced in this skill. If a required input is still missing, stop and ask the user for it before continuing. Use the shared scan artifact path conventions in ../../references/scan-artifacts.md.

Standard scans and Deep Scan workers assess attack paths within their ordinary Standard scan workflow; neither invokes this separate phase skill.

Compact Workbench-Backed Diff Mode

When a workbench-backed $security-diff-scan has a scanId, load the per-scan threat model and read the validated candidates with list_codex_security_candidates({ scanId, cursor?, limit? }). Analyze every reportable or deferred candidate, preserve every discovery and validation field and the original candidate order, and submit all decisions together with one record_candidate_attack_paths({ scanId, attackPaths: [{ candidateId, attackPath }] }) call. Submit attackPaths: [] when no candidate enters this phase. The existing tool atomically updates the stored candidates; do not create per-finding reports, receipts, or manual candidate ledgers in this compact diff mode. Keep attack-path facts, counterevidence, severity calibration, and policy adjustment as separate reasoning steps. Other scan and standalone workflows retain their existing artifact behavior.

Workflow

  1. Load the per-scan threat model path from ../../references/scan-artifacts.md as the repo-specific threat-model source of truth. Start from this along with the potential findings. Both inputs are required for this workflow.
    • For repository-wide and scoped-path scans, include validation closure rows marked reportable or survives: yes even if they were not assigned polished candidate numbers during discovery.
  2. Determine whether the affected code is in scope for the repository threat model and whether it belongs to a product surface or production workflow.
  3. Build a factual attack path using repository evidence only:
    • service mapping
    • exposure and entry points
    • identity, privilege, and trust boundaries
    • secrets handling and sensitive-data flow
    • reachability
    • existing controls and mitigations
  4. Before finalizing scope or reportability-driving facts, identify the strongest repository counterevidence against the key scoping fields and explain why it is or is not dispositive.
  5. Calibrate impact and likelihood from the repository evidence.
  6. Apply a separate final policy-adjustment pass mechanically using those facts and the calibrated severity.
  7. Record final policy decision ignore explicitly; in compact diff mode, retain its candidate record for coverage, and otherwise drop it from the surviving finding set.
  8. For a durable diff scan, submit the nested decision for every eligible candidate in the single compact tool call. Otherwise, save that finding's visible attack-path report and append one attack-path receipt per candidate id at the default paths from ../../references/scan-artifacts.md. The receipt must record the candidate id, attack-path reportability decision, attack-path facts or exact proof gap, and attack-path artifact/report reference for that candidate finding.

Read the full file on GitHub · 115 lines

Files

What ships with it

3 files 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. 1mo ago First seen · 115 lines · 60 tokens per session scan A 63e820ce598d

Subscribe to this mod's changes

attack-path-analysis is a skill published in the GitHub repository vlinx-io/VelaTerm (301 stars, last pushed 2d ago), licensed MIT. It adds 60 tokens to every session and 1,608 once invoked, about $0.0002 per session on Opus 5.5. 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 2026-09-14.

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