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.
npx skills add EresusSecurity/appsec-skills --skill eresus-threat-modelergit clone --depth 1 https://github.com/EresusSecurity/appsec-skillsWrote 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.
[](https://agentmods.dev/skills/eresussecurity/appsec-skills/eresus-threat-modeler)<a href="https://agentmods.dev/skills/eresussecurity/appsec-skills/eresus-threat-modeler"><img src="https://agentmods.dev/badge/skills/eresussecurity/appsec-skills/eresus-threat-modeler/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.
<a href="https://agentmods.dev/skills/eresussecurity/appsec-skills/eresus-threat-modeler"><img src="https://agentmods.dev/badge/skills/eresussecurity/appsec-skills/eresus-threat-modeler.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00084 | $0.00885 |
| Opus 5 | $0.00042 | $0.00443 |
| Sonnet 5 | $0.00017 | $0.00177 |
| Haiku 4.5 | $0.00008 | $0.00089 |
Grade A, and why
eresus-threat-modeler 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 10d 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.
How it starts
The opening of the file, as written. The whole thing — 144 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Threat Modeling
Purpose
Build a practical threat model that helps an AI agent focus security work on the highest-risk paths. Use it before implementation, during feature review, or when deciding which vulnerability classes and tests deserve the most attention.
Core Questions
Always answer:
- What assets matter here?
- Who can interact with them?
- Where does trust change?
- How could an attacker abuse the feature?
- Which controls must exist before release?
Workflow
Step 1: Define Scope
Clarify the target:
- single endpoint or workflow
- feature or service
- whole repository or subsystem
List the important assets: credentials, tokens, money movement, files, PII, admin actions, internal services, signed data, and audit logs.
Step 2: Map Actors and Entry Points
Identify:
- anonymous users
- authenticated users
- admins/support/internal operators
- third-party systems and webhooks
- scheduled jobs, queues, and background workers
Then list the entry points they can influence: HTTP routes, message queues, uploads, config, headers, mobile deep links, admin tooling, and internal RPC calls. Include serialized state channels such as cookies, session blobs, cache objects, queue payloads, and import/export formats.
Step 3: Draw Trust Boundaries
Mark every boundary where data becomes more trusted or more powerful, for example:
- browser to server
- public API to internal service
- user tenant to shared resource
- webhook sender to processing pipeline
- app server to database, cache, filesystem, or cloud metadata service
Step 4: Enumerate Abuse Cases
For each boundary, ask:
- Can identity be spoofed?
- Can an object/action be accessed without proper authorization?
- Can untrusted input reach code execution, queries, templates, files, or outbound network calls?
- Can untrusted input reach deserializers, object mappers, YAML/XML parsers, or state restore mechanisms?
- Can state transitions be raced, replayed, or skipped?
- Can the feature leak secrets, tokens, or cross-tenant data?
- Can cheap requests trigger expensive work?
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.
- 10d ago First seen · 144 lines · 84 tokens per session scan A 8724404078bd
eresus-threat-modeler is a skill published in the GitHub repository EresusSecurity/appsec-skills (7 stars, last pushed 5mo ago), licensed Apache-2.0. It adds 84 tokens to every session and 885 once invoked, about $0.0004 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 2026-08-31.
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