LLM Firewall
What is LLM Firewall?
LLM FirewallA security control that sits between users and a large language model to inspect prompts, retrieved context, and outputs in real time, blocking or rewriting traffic that violates policy.
An LLM firewall — also marketed as an AI firewall or LLM gateway — applies inspection, classification, and policy enforcement to traffic flowing in and out of LLM endpoints. Typical capabilities include prompt-injection and jailbreak detection, PII and secret redaction, content moderation, schema and tool-call validation, rate limiting per identity, RAG egress control, and detailed audit logs. Products and open-source projects in this space include LLM Guard, Prompt Shield, Lakera Guard, and vendor offerings from Cloudflare, F5, Palo Alto, and the major hyperscalers. LLM firewalls complement but do not replace guardrails inside the model, secure RAG design, identity-aware access control, and MLSecOps practices. They are most effective in defence-in-depth architectures where they enforce organisation-specific policy on top of vendor safety.
● Examples
- 01
A gateway that blocks prompts containing customer credit-card numbers before they reach the LLM API.
- 02
An LLM firewall that strips a known prompt-injection signature from a document before it is added to a RAG context.
● Frequently asked questions
What is LLM Firewall?
A security control that sits between users and a large language model to inspect prompts, retrieved context, and outputs in real time, blocking or rewriting traffic that violates policy. It belongs to the AI & ML Security category of cybersecurity.
What does LLM Firewall mean?
A security control that sits between users and a large language model to inspect prompts, retrieved context, and outputs in real time, blocking or rewriting traffic that violates policy.
How does LLM Firewall work?
An LLM firewall — also marketed as an AI firewall or LLM gateway — applies inspection, classification, and policy enforcement to traffic flowing in and out of LLM endpoints. Typical capabilities include prompt-injection and jailbreak detection, PII and secret redaction, content moderation, schema and tool-call validation, rate limiting per identity, RAG egress control, and detailed audit logs. Products and open-source projects in this space include LLM Guard, Prompt Shield, Lakera Guard, and vendor offerings from Cloudflare, F5, Palo Alto, and the major hyperscalers. LLM firewalls complement but do not replace guardrails inside the model, secure RAG design, identity-aware access control, and MLSecOps practices. They are most effective in defence-in-depth architectures where they enforce organisation-specific policy on top of vendor safety.
How do you defend against LLM Firewall?
Defences for LLM Firewall typically combine technical controls and operational practices, as detailed in the full definition above.
What are other names for LLM Firewall?
Common alternative names include: AI firewall, LLM gateway.
● Related terms
- ai-security№ 618
LLM Guardrails
Mechanisms that constrain what an LLM-based application can input or output, enforcing safety, security, and business rules around the underlying model.
- ai-security№ 866
Prompt Injection
An attack that overrides an LLM's original instructions by smuggling adversarial text into the prompt, causing the model to ignore safeguards or execute attacker-chosen actions.
- ai-security№ 777
OWASP LLM Top 10
An OWASP-maintained list of the ten most critical security risks affecting applications that build on large language models.
- ai-security№ 898
RAG Security
The discipline of securing retrieval-augmented generation pipelines so that the documents, vector stores, and retrieval steps that feed an LLM cannot be poisoned, abused, or used to exfiltrate data.
- ai-security№ 027
AI Governance
The policies, processes, roles, and controls organisations and regulators use to ensure AI systems are developed, deployed, and operated responsibly and lawfully.
- ai-security№ 691
MLSecOps
The discipline of integrating security and risk controls across the entire machine-learning lifecycle, from data sourcing through training, deployment, monitoring, and retirement.