Prompt Security alternative: spend firewalls for the agent economy
Prompt Security protects your LLM from injection attacks and data leaks. sipi.bot protects your wallet from runaway agent spending. Both are guardrails — one for security, one for finance — and both belong in a production agent stack.
Input security vs output security vs spend security
Prompt Security sits at the input layer — inspecting prompts for injection attempts, detecting attempts to exfiltrate data, and enforcing organizational security policies on LLM usage. sipi.bot sits at the action layer — when the agent decides to spend money, the firewall evaluates the transaction before it executes.
A complete agent security posture has three layers:
- Input guardrails (Prompt Security): what goes into the model is safe and policy-compliant.
- Output guardrails (Moderation API, Guardrails AI): what comes out of the model is safe and valid.
- Action guardrails (sipi.bot): what the agent does — specifically, what it spends — is within your financial policy.
Skip any layer and you have a gap. Prompt Security catches the injection attack that tries to trick the agent into revealing secrets; sipi.bot catches the transaction the agent makes after a successful prompt injection convinces it to buy from a malicious vendor.
Frequently asked
Does Prompt Security prevent agent overspending?
Prompt Security prevents prompt-level attacks but does not enforce financial policy — per-transaction caps, daily totals, or merchant rules. A legitimate agent with no injection can still overspend; sipi.bot catches that.
Can a prompt injection bypass sipi.bot?
No, because sipi.bot evaluates the transaction itself — the amount, the merchant, the category — not the prompt that generated it. Even if an injection convinces the agent to spend, the firewall blocks the transaction if it violates your rules.
How do sipi.bot and Prompt Security work together?
They're complementary security layers. Prompt Security = protect the LLM from attacks. sipi.bot = protect your money from overspend. Both are necessary for production agents that handle sensitive data and spend real money.