SkillShield vs AgentSeal: Two Layers of AI Agent Security
SkillShield Research Team
Security Research
When people ask "is my AI agent secure?" they usually mean one of two very different things. One is behavioral — can an attacker trick my running agent into leaking its system prompt or executing injected instructions? The other is supply-chain — is any of the software my agent installed before it ever ran carrying something it shouldn't?
These are separate threat surfaces. They need separate tools. AgentSeal covers the first. SkillShield covers the second. Understanding which you need — and why both matter — starts with understanding where each attack actually happens.
What AgentSeal does
AgentSeal is an open-source security scanner for AI agents. It works by firing 191+ attack probes at a running agent — either against a system prompt you provide, or against a live endpoint. It tests for two core attack categories:
Prompt extraction: Can an attacker get your agent to reveal its hidden system instructions? AgentSeal tests this with dozens of techniques: direct asks, social engineering framings, encoding tricks, roleplay misdirections, and more.
Prompt injection: Can an attacker override your agent's instructions and make it do something it shouldn't? AgentSeal tests injection resistance across persona hijacks, nested instruction frames, and encoding-based bypass attempts.
You get a trust score from 0 to 100, a breakdown by attack category, and specific recommendations. It installs in one command:
pip install agentseal
# or
npm install agentsealAnd you can run it against a prompt, a local model, or a live HTTP endpoint. No AI expertise required.
AgentSeal is a clean, well-scoped tool for what it does. If your threat model is "someone will try to manipulate my agent while it's running," AgentSeal is how you find out if it works.
What SkillShield does
SkillShield operates upstream of runtime. It scans the MCP skills, tools, and plugins your agent installs — before they execute anything.
The attack surface here is different. The question isn't "can an attacker manipulate my agent's behavior during a conversation?" It's "did something in my agent's skill set get tampered with before my agent even started?"
This matters because:
- MCP servers can be modified after you trust them. A skill you installed last week might not be the same code today. SkillShield tracks supply-chain changes against known-good baselines.
- Malicious skills can exfiltrate data passively. A skill doesn't need to manipulate your agent's conversation to be dangerous. It can silently read memory, intercept tool results, or make outbound calls during normal task execution.
- Tool poisoning is invisible to runtime scanners. If the skill itself is the threat vector, probing your agent's chat interface won't surface it. You need to inspect what the skill is doing at the code level.
The Agents of Chaos paper — a two-week adversarial lab run by 38 researchers on a live OpenClaw deployment — documented this distinction clearly. Attack categories like cross-agent propagation, unauthorized compliance, and indirect PII extraction don't originate from prompt injection. They originate from compromised or malicious skills operating inside a trusted permission boundary.
AgentSeal wouldn't catch those attacks. SkillShield is built specifically for them.
The two-layer model
Here's the cleanest way to think about it:
| Layer | Threat | Tool |
|---|---|---|
| Runtime (behavioral) | Prompt extraction, prompt injection, persona hijack | AgentSeal |
| Supply chain (install-time) | Malicious skills, tampered MCP servers, tool poisoning | SkillShield |
Neither tool replaces the other. An agent that passes AgentSeal's 191 probes can still be compromised at the supply-chain layer. An agent running only SkillShield-verified skills can still be vulnerable to runtime manipulation if its system prompt is weak.
The Agents of Chaos paper catalogued 11 distinct attack categories — and they map across both layers. Attacks like indirect prompt injection hit the runtime layer. Attacks like identity spoofing, cross-agent tool propagation, and unauthorized memory access hit the supply-chain layer.
A production AI agent needs both checked.
When to use each
Use AgentSeal when:
- You're finalizing a new agent's system prompt and want to know how manipulation-resistant it is
- You're preparing for an external security review and need a trust score
- You want to add agent security checks to CI/CD before deployment
Use SkillShield when:
- Your agent uses MCP skills, plugins, or external tools
- You're running on OpenClaw or any platform that supports skill/tool installs
- You've added new skills recently and want to verify supply-chain integrity
- You need continuous monitoring, not a one-time scan
Use both when:
- You're building production agents that touch sensitive data, external APIs, or user trust contexts
- You want to be able to say you've checked the full attack surface — not just the part that's easy to probe
Where things are today
AgentSeal is early-stage — 77 stars, actively maintained, Python and JavaScript support. It's a well-scoped tool solving a specific problem. We expect it to grow.
SkillShield is in active development with a live scanning backend. The right question isn't "which scanner should I use?" It's "which parts of my attack surface have I actually checked?" Most teams haven't checked either.
SkillShield is an AI agent supply-chain security scanner. For context on what AI agent attacks look like in a real lab, read Agents of Chaos: 11 AI Agent Security Vulnerabilities Exposed in an OpenClaw Lab.
Check both layers of your attack surface
SkillShield scans MCP skills and plugins at the supply-chain layer — the part most teams haven't checked. Get early access and know what your agent installed.
Get early access