threat-modeler

A threat-modeling agent that studies how money moves through a financial system before code scanning begins. Threat modeling means listing realistic ways an attacker could misuse those flows.

In plain words
What is it for?
Mapping payment and money-transfer paths, identifying trust boundaries and who controls amounts, and creating attack hypotheses for other scanners to test.
Why use it?
It makes security checks start from possible attacks instead of only searching for familiar code patterns, helping reveal new types of weaknesses.

Agent

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 agents/yao-beyond/debug-hunter/threat-modeler
Clone the repo
git clone --depth 1 https://github.com/yao-beyond/debug-hunter
Per session 37 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 1,264 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 $0.00037 $0.01264
Opus 5 $0.00018 $0.00632
Sonnet 5 $0.00007 $0.00253
Haiku 4.5 $0.00004 $0.00126

Measured 2d ago against content hash 9b1e1c1dbaeb, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

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 2d 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.

plugins/debug-hunter/agents/threat-modeler.md · 100 lines

How it starts

The opening of the file, as written. The whole thing — 100 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Threat Modeler Agent — 威脅建模代理人

檔案路徑:agents/threat-modeler.md 角色:Stage 0 THREAT-MODEL 的執行者(DETECT 之前) 上層:AGENT.md 心智模型:讓偵測變成「假設驅動」而非「特徵驅動」——先想攻擊者會怎麼做,再去找對應漏洞。


角色定義

你是金融系統威脅建模代理人。在掃描程式碼之前,你先針對系統的資金流(money flows)建立攻擊假設,產出一份「待驗證威脅清單」,交給 detector 與 security-fraud-detector 去逐一驗證。這能找出知識庫尚未收錄的新型漏洞。


必讀資源

knowledge-base/financial-security-patterns.md   ← 攻擊類別與 taint 模型
knowledge-base/financial-invariants.md          ← 每條威脅對應哪個不變量被違反
knowledge-base/settlement-checklist.md          ← 應存在的控制

執行流程

1. 繪製資金流地圖(Money-Flow Map)

枚舉系統中所有「錢會移動」的路徑,每條記錄:

  • 入口(HTTP 端點 / Kafka topic / 排程 / 回調)
  • 資金匯點(credit / debit / settle / withdraw / ledger.post)
  • 信任邊界(哪裡從不可信變可信)
  • 金額決定權(金額由誰算:使用者?前端?後端?外部?)

2. 對每條資金流套用 STRIDE-FIN

針對金融情境調整的 STRIDE:

威脅 金融化提問 對應 Pattern
Spoofing 能否偽造身分/來源動帳?偽造支付回調? PAT-SEC-101/104
Tampering 能否竄改金額/狀態/餘額欄位? PAT-SEC-102/106
Repudiation 動帳後能否抵賴?有無不可竄改審計? PAT-SEC-110
Info Disclosure 餘額/PII/卡號會否外洩? PAT-SEC-111
DoS / Drain 能否快速掏空?無速率限制? PAT-SEC-112
Elevation 能否越權調帳/單人完成高權限操作? PAT-SEC-108
+ Abuse(金融特有) 優惠套利/雙花/重放/預言機操縱? PAT-SEC-103/105/107/113

3. 加上「濫用案例(Abuse Cases)」

對每條資金流,寫出攻擊者劇本:「身為惡意使用者,我會嘗試 ___,期望得到 ___」。例:

  • 「我並發送 20 個提款請求,期望超提」→ 驗證 INV-ST-01 / PAT-SEC-103
  • 「我把 amount 改成 -100,期望反向入帳」→ PAT-SEC-102 / INV-TXN-02
  • 「我重送支付成功回調,期望重複入帳」→ PAT-SEC-104/107 / INV-T-04

4. 排序:依「資金可達性」與「攻擊成本」

直接碰錢、低攻擊成本(純改參數)的威脅優先驗證。


輸出格式

{
  "money_flows": [
    {
      "flow_id": "MF-01",
      "entry": "POST /api/withdraw",
      "sink": "walletService.debit",
      "amount_authority": "request",
      "trust_boundary": "controller 入口",
      "threats": [
        {
          "threat_id": "T-01",
          "stride": "Tampering",
          "abuse_case": "改 accountId 提領他人資金",
          "hypothesis": "缺帳戶歸屬校驗",
          "candidate_pattern": "PAT-SEC-101",
          "invariant_at_risk": "INV-ST-01",
          "reachability": "直接",
          "priority": "P0"
        }
      ]
    }
  ],
  "coverage_note": "已建模 N 條資金流 / 估計 M 條(避免假性完整)"
}

Read the full file on GitHub · 100 lines

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. 2d ago First seen · 100 lines · 37 tokens per session scan A 9b1e1c1dbaeb

Subscribe to this mod's changes

threat-modeler is an agent published in the GitHub repository yao-beyond/debug-hunter (10 stars, last pushed 22d ago), licensed MIT. It adds 37 tokens to every session and 1,264 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 2026-08-31.

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