PromptPurify
Overview
Product details compiled from public sources, each with a citation.
- Vendor
- SecureLayer71agent
- Description
- Open-source prompt-injection guard for LLM chat applications, pairing a compact CPU-only classifier with a deterministic structural firewall.1agent
- Deployment
- Self-hosted1agent
- Status
- Active1agent
Matrix Coverage
Where this product defends, by asset class and NIST CSF function. The Coverage column shows whether each asset is Primary, Secondary, or Adjacent to what the product does. The table omits empty rows and columns.
| Asset class | Protect | Detect | Coverage | Source |
|---|---|---|---|---|
| Runtime AI Data | Primary | 1 |
Framework Relevance
These frameworks include controls relevant to the asset classes PromptPurify defends. This is an editorial inference from the AI Defense Matrix asset-level crossmap, not a statement that SecureLayer7 implements these controls or is certified against them.
Expand Collapse
| Framework | Asset class | Relevant controls |
|---|---|---|
| NIST IR 8596 | Runtime AI Data | Prompts (runtime); inference data |
| CSA AI Controls Matrix | Runtime AI Data | Data Security and Privacy Lifecycle Management; Application and Interface Security |
| ISO 42001 | Runtime AI Data | A.7 Data for AI systems; A.8 Information for interested parties |
| Google SAIF | Runtime AI Data | Expand AI red-teaming; runtime input and output safety; prompt defense |
| SANS Critical AI Security Guidelines | Runtime AI Data | Model I/O Handling (sanitize, validate, and filter inputs and outputs; segregate user and system prompts; multilayered prompt-injection defense); Conventional Security Controls (protect augmentation and RAG data with vector-store access controls and validation); Data Minimization and Obfuscation (limit sensitive prompt content; context-window management); Limit Model Behavior (AI guardrails) |
| MITRE ATLAS | Runtime AI Data | AML.T0051 LLM Prompt Injection; AML.T0054 LLM Jailbreak; AML.T0056 Extract LLM System Prompt |
| OWASP AI Exchange | Runtime AI Data | Input threats: prompt injection, adversarial inputs, evasion; runtime threats: RAG poisoning, memory tampering |
| OWASP LLM Top 10 | Runtime AI Data | LLM01 Prompt Injection; LLM02 Sensitive Information Disclosure; LLM08 Vector and Embedding Weaknesses; LLM05 Improper Output Handling |
| OWASP Agentic Security Top 10 | Runtime AI Data | ASI06 Memory & Context Poisoning; ASI01 Agent Goal Hijack (via prompt injection in runtime inputs) |
Provenance
Last sourced 2026-09-03.
Expand Collapse
Sources
- PROMPTPurify repository
- “Drop-in guard between your user input and your LLM”
Changelog
-
Added to the catalog from the PROMPTPurify repository documentation.
Found an error? Corrections are welcome. Suggest an edit.