Prompt Injection Defenses

Authored by:

munamwasi

jubos

masterfung

Reviewed by:

matta

The Red Guild | SEAL

As LLMs execute based on statistical patterns rather than explicit checks, malicious or cleverly structured prompts can cause the model to ignore safety instructions, reveal sensitive data, or perform unintended actions. Effective mitigation requires intercepting and sanitizing inputs at the execution layer rather than relying solely on upstream policies or prompt templates. Security controls should classify and constrain inputs before they are interpreted by a model.

On-Chain Data as Untrusted Input

In smart contract tooling, DAO governance assistants, and wallet agents, prompt injection can lead to incorrect transaction construction or misleading governance actions. Inputs originating from on-chain data or community proposals should be treated as untrusted by default.

Consider using

• CrowdStrike Falcon AI Detection and Response - tracks 150+ prompt injection techniques
• Mighty - multimodal prompt injection security API at model ingress and egress
• Prompt Security - runtime enforcement for agentic AI with cross-agent policy controls
• Lakera Guard - real-time prompt injection detection with dynamic threat intelligence
• Robust Intelligence (Cisco) - AI validation plus AI firewall guardrail enforcement
• Imperva AI Application Security - LLM-focused protection beyond traditional WAF assumptions