Detect and block prompt injections from user-generated content
Public-facing agents can ingest comments, product descriptions, or feedback fields with embedded injections. Guardrails neutralize unsafe inputs before they reach the LLM.
What's at stake
- Any field a user can edit becomes a potential injection vector
- Product descriptions, comments, reviews, and feedback forms can all carry attack payloads
- A successful injection can manipulate agent behavior across all users who encounter that content
- Attacks can persist: malicious content in a database continues to attack every future interaction
- Public-facing agents are especially vulnerable to coordinated injection campaigns
How to solve this
When your agent processes user-generated content—product descriptions, comments, reviews, form submissions—it treats that content as context. If an attacker embeds instructions in their "product review," your agent might follow those instructions instead of its original task.
The solution is to scan all user-generated content before it enters agent context. This requires distinguishing between legitimate user content and adversarial instructions. The challenge: normal content and attack content can look similar at the surface level.
Effective detection uses both pattern matching (known attack signatures) and semantic analysis (understanding when content is trying to influence agent behavior). Content that passes inspection is safe to process; detected attacks are blocked or sanitized.
How Superagent prevents this
Superagent provides guardrails for AI agents—small language models purpose-trained to detect and prevent failures in real time. These models sit at the boundary of your agent and inspect inputs, outputs, and tool calls before they execute.
For user-generated content, Superagent's Guard model scans every piece of external input before it reaches your agent. Guard analyzes text for prompt injection patterns—attempts to override instructions, exfiltrate context, or manipulate behavior.
The model is trained on thousands of real-world injection attempts and their variations. It recognizes attacks even when obfuscated, encoded, or disguised as legitimate content. When an injection is detected, Guard blocks the content and logs the attempt.
Guard runs in real-time with minimal latency. Your agent continues processing legitimate user content normally while attacks are filtered out. Detection events feed into your security monitoring for pattern analysis and response.