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Map all 754 skills to MITRE ATT&CK v19.1

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- Add validated mitre_attack frontmatter to all 754 skills (286 distinct
  techniques), verified against MITRE ATT&CK v19.1 via the official
  mitreattack-python library: 0 revoked, deprecated, or invalid IDs
- Curate precise per-skill technique IDs for forensics, malware-analysis,
  threat-intel, and red-team skills (e.g. DCSync -> T1003.006,
  Kerberoasting -> T1558.003, Pass-the-Ticket -> T1550.003)
- Reconcile v19.1 tactic restructuring: Defense Evasion split into
  Stealth (TA0005) and Defense Impairment (TA0112); revoked T1562.*
  family and T1070.001/.002 remapped to active equivalents (T1685.*)
- Normalize word-split tags across 35 skills (remove filename-derived
  stopword tags, add semantic cybersecurity tags)
- Add api-reference.md for 3 skills that were missing it
- Update README ATT&CK section with accurate v19.1 tactic distribution
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mukul975
2026-06-01 12:13:29 +02:00
parent 9a588e643e
commit cb8d79e068
755 changed files with 7832 additions and 2286 deletions
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skills/implementing-llm-guardrails-for-security/SKILL.md
+15 -7
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@@ -1,12 +1,15 @@
---
name: implementing-llm-guardrails-for-security
description: 'Implements input and output validation guardrails for LLM-powered applications to prevent prompt injection,
data leakage, toxic content generation, and hallucinated outputs. Builds a security validation pipeline using NVIDIA NeMo
Guardrails Colang definitions, custom Python validators for PII detection and content policy enforcement, and the Guardrails
AI framework for structured output validation. The guardrails system intercepts both user inputs (blocking injection attempts,
stripping PII, enforcing topic boundaries) and model outputs (detecting hallucinations, filtering toxic content, validating
JSON schema compliance). Activates for requests involving LLM output validation, AI content filtering, guardrail implementation,
or LLM safety enforcement.
description: 'Implements input and output validation guardrails for LLM-powered applications
to prevent prompt injection, data leakage, toxic content generation, and hallucinated
outputs. Builds a security validation pipeline using NVIDIA NeMo Guardrails Colang
definitions, custom Python validators for PII detection and content policy enforcement,
and the Guardrails AI framework for structured output validation. The guardrails
system intercepts both user inputs (blocking injection attempts, stripping PII,
enforcing topic boundaries) and model outputs (detecting hallucinations, filtering
toxic content, validating JSON schema compliance). Activates for requests involving
LLM output validation, AI content filtering, guardrail implementation, or LLM safety
enforcement.
'
domain: cybersecurity
@@ -43,6 +46,11 @@ nist_csf:
- ID.RA-01
- PR.PS-01
- DE.AE-02
mitre_attack:
- T1078
- T1190
- T1059
- T1055
---
# Implementing LLM Guardrails for Security