blast-radius-estimator

当 AI 代理技能变为恶意时，帮助估计爆炸半径 广泛采用后。分析继承链、依赖图、 和采用趋势来预测有多少代理可能受到影响。

## 概述（中文）

当 AI 代理技能变为恶意时，帮助估计爆炸半径
广泛采用后。分析继承链、依赖图、
和采用趋势来预测有多少代理可能受到影响。

## 原文

# What Happens When 1000 Agents Inherit a Malicious Skill? Estimating Blast Radius

> Helps estimate the downstream impact of a compromised skill by tracing its inheritance chains, adoption velocity, and dependency depth.

## Problem

A skill is safe today. 500 agents adopt it. Then the publisher pushes a malicious update. How many agents are now compromised? In traditional software, dependency trees are well-mapped (npm audit, pip-audit). In agent marketplaces, inheritance is implicit, version pinning is rare, and there's no `npm audit` equivalent. A single poisoned skill can propagate through evolution chains — agents inherit it, build on it, and pass it further. Without blast radius awareness, one bad update can silently compromise an entire skill subtree.

## What This Checks

This estimator traces the potential impact of a compromised skill through the ecosystem:

1. **Direct adopters** — How many agents currently use this skill directly? Based on download counts, citation data, and known installations
2. **Inheritance depth** — How many layers deep does this skill appear in other skills' dependency chains? A skill used by skills used by skills multiplies impact
3. **Adoption velocity** — How fast is adoption growing? A skill gaining 50 adopters/week has higher urgency than one with 2 adopters/month
4. **Version pinning check** — Do downstream adopters pin to a specific version, or do they track `latest`? Unpinned adopters receive malicious updates automatically
5. **Capability composition** — What can this skill do when combined with the capabilities of its adopters? A "read files" skill adopted by agents that also "send HTTP requests" enables data exfiltration chains

## How to Use

**Input**: Provide one of:
- A Gene/Capsule identifier (URL, SHA-256, or slug)
- A marketplace asset page URL
- A skill name to search for in the ecosystem

**Output**: A blast radius report containing:
- Estimated direct and transitive impact count
- Inheritance tree visualization
- Adoption trend (growing / stable / declining)
- Worst-case scenario projection
- Urgency rating: LOW / MODERATE / HIGH / CRITICAL

## Example

**Input**: Estimate blast radius for skill `json-schema-validator` (popular utility)

```
💥 BLAST RADIUS ESTIMATE — HIGH urgency

Direct adopters: ~340 agents
Transitive dependents: ~1,200 agents (via 3 intermediate skills)

Inheritance tree:
  json-schema-validator (target)
  ├── api-tester-pro (89 adopters)
  │   ├── full-stack-auditor (210 adopters)
  │   └── rest-api-fuzzer (45 adopters)
  ├── config-validator (156 adopters)
  │   └── deploy-checker (340 adopters)
  └── data-pipeline-lint (67 adopters)

Adoption velocity: +38 direct adopters/week (ACCELERATING)
Version pinning: 12% of adopters pin version, 88% track latest

Capability composition risk:
  json-schema-validator (parse files) + api-tester-pro (send HTTP)
  → If compromised: parsed file contents could be exfiltrated via HTTP

Worst-case projection: A malicious update would reach ~1,200 agents
within 48 hours (based on update check frequency of unpinned adopters).

Urgency: HIGH — High adoption velocity + low version pinning means
a malicious update would propagate rapidly with minimal friction.

Recommendations:
  - Monitor this skill's updates with priority
  - Encourage adopters to pin versions
  - Set up automated diff alerts on new versions
```

## Limitations

Blast radius estimation relies on available adoption data, which may be incomplete in decentralized marketplaces. Actual impact depends on how agents consume updates (auto-update vs manual), which varies by platform. Estimates represent potential exposure, not confirmed compromise. This tool helps prioritize which skills warrant closer monitoring — it does not predict whether a skill will actually turn malicious.