Your Agent Has 12 Skills — Do You Know What They Can Do Together?
Helps map composite permission surfaces across skill dependency chains, revealing emergent capabilities that no single skill declares.
Problem
Individual skill permissions look reasonable in isolation. A file-reader skill reads files. An HTTP client skill sends requests. A JSON parser skill transforms data. Each one passes a security review on its own.
But install all three in the same agent, and you've built a data exfiltration pipeline — read sensitive files, parse out credentials, send them to an external endpoint. Nobody approved that combination. Nobody even noticed it exists.
In traditional software, tools like npm audit map dependency trees and flag known vulnerabilities. In agent ecosystems, the risk isn't in individual dependencies — it's in the composite capability surface that emerges when skills combine. There is no npm audit for emergent agent capabilities.
What This Maps
This mapper traces the permission graph across an agent's installed skills:
- 1. Permission enumeration — For each skill, extract declared capabilities: file access, network requests, shell execution, environment variable reads, credential access
- Pairwise composition — For every pair of skills, check if their combined capabilities create a new emergent capability (e.g., read + send = exfiltrate)
- Transitive chains — Trace three-hop and deeper composition paths where skill A feeds skill B feeds skill C, creating capabilities invisible at any single hop
- Privilege surface score — Compute a single metric: how many distinct dangerous capability combinations exist in this agent's skill set?
- Delta analysis — When a new skill is added, show what new composite capabilities it introduces to the existing set
How to Use
Input: Provide one of:
- - A list of skill names/slugs installed in an agent
- A skill manifest or configuration file
- A single skill to evaluate against a known agent profile
Output: A capability graph report containing:
- - Permission matrix (skills × capabilities)
- Emergent capability combinations flagged as risky
- Privilege surface score (0-100)
- Recommendation: which skill combinations to review manually
- Delta report if evaluating a new addition
Example
Input: Map capability surface for agent with skills: log-analyzer, http-poster, env-reader, INLINECODE5
CODEBLOCK0
Related Tools
- - blast-radius-estimator — estimates downstream impact when a skill turns malicious; capability-graph-mapper helps quantify what a compromised skill could do
- permission-creep-scanner — checks individual skills for over-permission; this mapper checks what happens when multiple over-permissioned skills combine
- supply-chain-poison-detector — detects poisoned individual skills; this mapper shows why a poisoned skill with network access is more dangerous in agents that also have file-read skills
Limitations
Capability graph mapping depends on accurately extracting each skill's actual permissions, which may not always match declared permissions. Skills that dynamically request capabilities at runtime may not be fully captured through static analysis. The composition risk model uses known dangerous patterns (read+send, parse+execute) but novel attack chains may not be in the pattern library. This tool helps surface emergent risks for human review — it does not guarantee detection of all possible capability combinations. Privilege surface scores are relative, not absolute measures of risk.
技能名称: capability-graph-mapper
你的智能体拥有12项技能——你知道它们能协同做什么吗?
帮助映射跨技能依赖链的复合权限面,揭示任何单一技能都未声明的涌现能力。
问题
单个技能权限单独看是合理的。文件读取技能读取文件。HTTP客户端技能发送请求。JSON解析技能转换数据。每个技能都能独立通过安全审查。
但将这三个技能安装到同一个智能体中,你就构建了一条数据窃取管道——读取敏感文件、解析出凭证、发送到外部端点。没有人批准这种组合。甚至没有人注意到它的存在。
在传统软件中,像npm audit这样的工具可以映射依赖树并标记已知漏洞。在智能体生态系统中,风险不在于单个依赖项——而在于技能组合时产生的复合能力面。对于涌现的智能体能力,没有npm audit这样的工具。
映射内容
此映射器追踪智能体已安装技能间的权限图:
- 1. 权限枚举 — 对每个技能,提取声明的能力:文件访问、网络请求、shell执行、环境变量读取、凭证访问
- 成对组合 — 对每对技能,检查它们组合的能力是否产生新的涌现能力(例如,读取+发送=窃取)
- 传递链 — 追踪三跳及更深层次的组合路径,其中技能A供给技能B,技能B供给技能C,产生任何单跳都不可见的能力
- 权限面评分 — 计算单一指标:该智能体技能集中存在多少种不同的危险能力组合?
- 增量分析 — 当添加新技能时,显示它为现有集合引入了哪些新的复合能力
使用方法
输入:提供以下之一:
- - 智能体中安装的技能名称/标识列表
- 技能清单或配置文件
- 针对已知智能体配置文件评估的单个技能
输出:包含以下内容的能力图报告:
- - 权限矩阵(技能×能力)
- 标记为有风险的涌现能力组合
- 权限面评分(0-100)
- 建议:需要手动审查的技能组合
- 如果评估新增技能,则提供增量报告
示例
输入:映射包含以下技能的智能体能力面:log-analyzer、http-poster、env-reader、markdown-formatter
🕸️ 能力图 — 检测到3个涌现风险
权限矩阵:
readfiles sendhttp readenv execshell write_files
log-analyzer ✓
http-poster ✓
env-reader ✓ ✓
markdown-formatter ✓ ✓
涌现能力组合:
⚠️ 风险1:数据窃取路径
env-reader(读取.env)→ http-poster(发送HTTP)
组合:可以读取凭证并将其传输到外部
严重程度:高
⚠️ 风险2:敏感文件中继
log-analyzer(读取日志)→ http-poster(发送HTTP)
组合:可以读取应用程序日志并将内容发送到外部
严重程度:中
⚠️ 风险3:三跳链
env-reader(读取密钥)→ markdown-formatter(转换数据)
→ http-poster(发送HTTP)
组合:在一个管道中读取、混淆和窃取
严重程度:高
权限面评分:67/100(偏高)
建议:
- 审查http-poster是否需要与env-reader共存
- 考虑对env-reader的文件访问范围进行沙箱化
- markdown-formatter → http-poster链可实现混淆;
审计markdown-formatter可以输出什么
相关工具
- - blast-radius-estimator — 估计技能变恶意时的下游影响;capability-graph-mapper帮助量化受损技能能做什么
- permission-creep-scanner — 检查单个技能是否权限过大;此映射器检查多个权限过大的技能组合时会发生什么
- supply-chain-poison-detector — 检测被投毒的单个技能;此映射器展示为什么具有网络访问权限的被投毒技能在同时拥有文件读取技能的智能体中更危险
局限性
能力图映射依赖于准确提取每个技能的实际权限,这可能并不总是与声明的权限一致。在运行时动态请求能力的技能可能无法通过静态分析完全捕获。组合风险模型使用已知的危险模式(读取+发送、解析+执行),但新型攻击链可能不在模式库中。此工具有助于呈现涌现风险供人工审查——它不能保证检测所有可能的能力组合。权限面评分是风险的相对度量,而非绝对度量。