LLMArmor vs Lakera Guard: Static Analysis vs Runtime Proxy
Lakera Guard is a commercial AI security platform that provides a runtime API proxy and firewall for LLM applications. Every request to your LLM passes through Lakera’s endpoint, which detects and blocks prompt injection attempts, PII leakage, toxic content, and other threats in real time. LLMArmor is an open-source static analysis tool that scans your Python source code for OWASP LLM Top 10 security misconfigurations before deployment.
These tools operate at fundamentally different layers and are complementary rather than competitive.
At a glance
Section titled “At a glance”| Dimension | LLMArmor | Lakera Guard |
|---|---|---|
| Type | Open-source static code scanner | Commercial runtime AI firewall / API proxy |
| Approach | Analyzes Python source files | Intercepts live LLM API requests |
| When it runs | At commit / CI time (pre-deploy) | At runtime, on every request |
| What it needs | Python source files | Integration into your LLM API call path |
| Latency added | Zero (offline analysis) | Network hop per request (~20–50ms typical) |
| Cost | Free, open source (MIT) | Commercial, usage-based pricing |
| Standards alignment | OWASP LLM Top 10 | Proprietary threat taxonomy + OWASP LLM |
| SARIF / GitHub Code Scanning | ✅ Built-in | ❌ Not applicable |
| Prompt injection detection | Static patterns in code | Dynamic classification of live prompts |
| PII detection | Hardcoded keys in source | PII in live request/response content |
| Vendor dependency | None (runs offline) | Lakera’s cloud service |
What Lakera Guard does well
Section titled “What Lakera Guard does well”Lakera Guard is purpose-built for runtime protection. Its classifiers evaluate the semantic content of prompts and responses as they flow through your system. It can detect:
- Indirect prompt injection hidden in retrieved documents (RAG poisoning)
- PII in user inputs or model outputs at the content level
- Jailbreak attempts that exploit model behavior at inference time
- Toxic or harmful content in either direction
For production systems handling untrusted user input, a runtime guard provides a safety net for attack patterns that evolve faster than static rules can track.
What LLMArmor does well
Section titled “What LLMArmor does well”LLMArmor finds code-level security misconfigurations that a runtime firewall cannot see:
- A developer accidentally interpolates
user_inputdirectly into a system prompt in source code — this is exploitable before a single user interacts with the app - An API key is hardcoded in a config file committed to the repository
- LLM output is passed to
eval()— this is a code bug, not a content issue - An agent tool has
auto_approve=True, removing human oversight - Missing
max_tokensmeans the app can be billed for unbounded token usage
These issues exist in the code. Lakera Guard, as a request proxy, cannot analyze source code and therefore cannot find these issues.
Key differences in threat coverage
Section titled “Key differences in threat coverage”| Threat | LLMArmor | Lakera Guard |
|---|---|---|
| User input interpolated into system prompt (code-level) | ✅ Detected statically | ❌ Cannot inspect source code |
| Live prompt injection in request content | ❌ Not a runtime tool | ✅ Classifies live requests |
| Hardcoded API key in source | ✅ Detected | ❌ Not applicable |
| PII in live request/response content | ❌ Not a runtime tool | ✅ Classifies content |
LLM output to eval()/subprocess | ✅ Detected statically | ❌ Does not analyze code paths |
| Indirect prompt injection via RAG | ❌ Cannot analyze retrieved docs | ✅ Classifies retrieved content |
Missing max_tokens on API calls | ✅ Detected statically | ❌ Does not inspect code |
| Agent wildcard tool access | ✅ Detected statically | ❌ Does not inspect code |
When to choose LLMArmor
Section titled “When to choose LLMArmor”- You want a free, open-source security check that runs in CI
- You need SARIF output for GitHub Code Scanning
- You’re auditing code for OWASP LLM Top 10 compliance
- You want to catch misconfigurations before they reach production
- You’re building an internal tool or working in a cost-sensitive environment
When to choose Lakera Guard
Section titled “When to choose Lakera Guard”- You need runtime protection against evolving prompt injection and jailbreak techniques
- You handle sensitive PII in user inputs or model outputs
- You’re building a customer-facing product where zero prompt injection is a hard requirement
- Your organization requires a commercial solution with SLA, support, and compliance certifications (SOC 2, etc.)
Recommendation
Section titled “Recommendation”For production LLM applications, the two tools are complementary:
- Use LLMArmor in CI to catch code-level misconfigurations before deployment — fast, free, no API cost.
- Use Lakera Guard in production to provide a runtime safety net against content-level threats that evolve post-deployment.
Lakera Guard does not make LLMArmor redundant, and LLMArmor does not make Lakera Guard redundant. Code-level issues and runtime content threats require different tools.