Prompt injection in AI agents requires a multi-layered defense. You need input separation, validation, hardening, and monitoring. This is critical for building **Liminality**, the question-decomposition and routing MCP server.
### 1. Input Separation and Structuring
The core vulnerability is failing to distinguish system instructions from untrusted user data.
* **Use Structured Prompt Templates:** Stop using raw string concatenation. Use explicit role-based formatting (system, user, assistant roles) from LLM APIs. * **Implement Delimiters:** Use clear delimiters between system instructions, user input, and retrieved context like RAG data. This forces the model to treat injected content as data, not instructions. * **Abstraction Layer:** Build an abstraction layer between user input and the foundation model. Sanitize and validate all inputs before they hit the LLM.
### 2. Input Validation and Detection
Deploy regex-based and heuristic detection. Block malicious patterns including Unicode homoglyphs, zero-width characters, and base64/hex encoding.
* **Recommended Libraries:** * **PromptShield (stlas/mthamil107):** Combines regex with ML (DeBERTa-v3) for semantic detection. It has 33 input detectors covering multiple languages and uses Smith-Waterman sequence alignment. * **Promptguard (JSLEEKR):** A lightweight, dependency-free regex scanner with a TypeScript API for custom rules. * **Sentinel-inject:** Uses a fast regex layer for known patterns and an optional LLM-assisted semantic layer for paraphrased attacks. * **PromptInjectionDetector (SaVi456):** Employs a three-layer approach: heuristic engine, TF-IDF/Logistic Regression classifier, and an LLM-based judge. * **Implementation:** Pass user input through a scan method that returns a risk score or boolean safety status. Add custom rules via YAML files or code definitions.
### 3. System Prompt Hardening
Mitigate context leakage and adversarial inputs through guardrails and configuration.
* **Guardrails:** Use tools like Amazon Bedrock Guardrails to detect prompt attacks, filter content, and enforce blocklists/allowlists. Configure guardrails in block mode for high-severity threats. * **Scope Enforcement:** Apply prompt attack detection to all input surfaces. This includes direct user messages, RAG retrieved content, and API responses. * **Resource Limits:** Set character and token limits on prompts. Implement rate limiting. * **Least Privilege:** Enforce least privilege access for agents. Sanitize sensitive data in prompts. * **LLM-as-a-Judge:** Use techniques to evaluate prompt safety programmatically.
### 4. Monitoring and Feedback
Implement real-time detection and feedback loops to identify potential injection attempts.
* **Real-Time Detection Tools:** * **AgentForensics:** Open-source framework for real-time session monitoring and attack fingerprinting using a five-stage pipeline (heuristic rules, DistilBERT ML classifier, instruction boundary analysis). * **PromptGuard:** Production-ready AI firewall providing real-time threat detection for prompt injection and PII leakage via a single API endpoint. * **Prompt-Shield:** Comprehensive engine with 33 input detectors, 9 output scanners, and self-learning feedback loops. * **SecuPrompt:** Deterministic firewall that sanitizes prompts by identifying role overrides and Unicode exploits. * **Basalt Shield:** Features a 5-layer architecture including vector-based attack memory and specialized protection for the Model Context Protocol (MCP). * **Prompt-Injection-Firewall (PIF):** Transparent reverse-proxy middleware using a hybrid ensemble of 129 regex patterns and a DistilBERT-based ML engine. * **SecureVector:** AI firewall for agents that scans requests/responses locally, offering per-tool permissions and OWASP-mapped threat detection. * **PromptSentinel:** Enterprise-grade detection providing SARIF-formatted output for SIEM integration.
### Application to Liminality
Since Liminality is an MCP server, **Basalt Shield** is particularly relevant due to its specialized protection for the Model Context Protocol. Given the routing nature of Liminality, **PromptGuard** or **Prompt-Shield** can be integrated as a middleware layer. This secures traffic between the LLM and various toolkits like Airtable, Asana, and Figma before execution.
This answer was worked out by Liminality — Physea's engine that decomposes a request, grounds each part to a real tool, and returns a reusable, checkable route.
Connect it over MCP: https://mcp.physea.ai/mcp · physea.ai