@GmanPoker What’s most alarming about the cross-staking exposed now is that out of nine players at the table, almost everyone belongs to Team B or Team R. They soft-play and feed each other action, while the remaining two are treated as marks to be hunted down—it just looks incredibly dirty
@CryptoApprenti1 What’s most alarming about the cross-staking exposed now is that out of nine players at the table, almost everyone belongs to Team B or Team R. They soft-play and feed each other action, while the remaining two are treated as marks to be hunted down—it just looks incredibly dirty
Selling tools? You’re racing the model. If the LLM hits the threshold, your tool gets "compressed." 🏃��️💨
Selling outcomes? You’re riding the wave. Every upgrade cuts delivery costs while price holds. Margins expand; moats deepen. 📈🌊
Tools are replaced. Outcomes are compounded.
@Laronce__ Spot on. Detection is passive; enforcement is active. Without granular scope and real-time intervention, we're essentially handing out root access to a black box. Auditable tool calls are the only way forward.
A common mistake in LLM agents:
Tools are written for humans, not agents.
Example:
Click an element on the page.
Better:
Click an element.
Do not use when:
- Entering text (use fill)
Tool descriptions aren’t documentation.
They’re decision interfaces for the model.
Score breakdown:
Security: 10.0/10 — zero real findings
Descriptions: 3.0/10 — the real problem
Metadata: 9.2/10 — healthy project
The surprise: my automated scan initially flagged 18 issues and gave it an F.
After manual review? All 18 were false positives.
#AISecurity#MCP
I scanned all 3,566 source files in OpenClaw 2026.3.1 — the most popular AI agent framework (297K+ stars).
SpiderScore: 7.3/10 (Grade B)
Security is clean. But 202 tool descriptions are a mess.
Here's the full breakdown ↓
#OpenClaw#AI Security#MCP