Claude 24f816b6a3
Consolidate 22 sibling repos into layered organism structure
Place useful parts of the surrounding repos into sica-fondt by layer, per the
body model (Ada = membrane; brain/endocrine/capabilities/knowledge non-Ada):

- brain/        LLM reasoning + providers (dapr, hermes, MoMoA)
- capabilities/ REPRAG sidecars: hermes tools/skills, dapr tools, parallel
                dispatch, A51 channels, and the OSINT cluster
- knowledge/    LORAG corpus: 754 cyber-skills, agency personas, secure-coding,
                MITRE ATT&CK data
- reference/    defensive threat-reference (C3, shhbruh doc) + AdaYaml parser

License handling: AGPL sources (worldosint, advanced_evolution, mercury,
Reticulum) and GPL DeTTECT are SPEC-only clean-room/port descriptions — no
copyleft code copied. MIT/Apache/data parts copied as working trees.

Safety: shhbruh escape/persistence material and C3 covert-C2 kept as reference
only, not wired into the running organism. See CONSOLIDATION.md.

https://claude.ai/code/session_01UehUqEXXJJCsHoA4voCU5c
2026-06-10 06:53:01 +00:00

2.9 KiB

name description domain subdomain tags version author license nist_ai_rmf nist_csf
implementing-runtime-application-self-protection Deploy Runtime Application Self-Protection (RASP) agents to detect and block attacks from within application runtime, covering OpenRASP integration, attack pattern detection, and security policy configuration for Java and Python web applications. cybersecurity application-security
rasp
application-security
openrasp
runtime-protection
sqli
xss
rce
devsecops
1.0 mahipal Apache-2.0
GOVERN-1.1
MEASURE-2.7
MANAGE-3.1
PR.PS-01
PR.PS-04
ID.RA-01
PR.DS-10

Implementing Runtime Application Self-Protection

Overview

Runtime Application Self-Protection (RASP) instruments application code at runtime to detect and block attacks by examining actual execution context rather than relying solely on network traffic patterns. Unlike WAFs that inspect HTTP requests externally, RASP agents intercept dangerous operations (SQL queries, file operations, command execution, deserialization) at the function level inside the application, achieving near-zero false positives. This skill covers deploying OpenRASP for Java applications, configuring detection policies for OWASP Top 10 attacks, tuning alerting thresholds, and integrating RASP telemetry with SIEM platforms.

When to Use

  • When deploying or configuring implementing runtime application self protection capabilities in your environment
  • When establishing security controls aligned to compliance requirements
  • When building or improving security architecture for this domain
  • When conducting security assessments that require this implementation

Prerequisites

  • Java 8+ application server (Tomcat, Spring Boot, or JBoss) or Python Flask/Django application
  • OpenRASP agent package (rasp-java or equivalent)
  • OpenRASP management console for centralized policy management
  • SIEM integration endpoint (Splunk HEC, Elasticsearch, or syslog)
  • Application staging environment for RASP testing before production

Steps

Step 1: Deploy RASP Agent

Install the RASP agent into the application server runtime using JVM agent attachment for Java or middleware hooks for Python.

Step 2: Configure Detection Policies

Define detection rules for SQL injection, command injection, SSRF, path traversal, XXE, and deserialization attacks with block or monitor actions.

Step 3: Tune and Baseline

Run the agent in monitor mode during normal operations to establish baseline behavior and tune policies to reduce false positives before switching to block mode.

Step 4: Integrate with SIEM

Forward RASP alerts to the SIEM for correlation with WAF, IDS, and authentication events to build comprehensive attack timelines.

Expected Output

JSON report containing RASP policy audit results, detected attack attempts with stack traces, blocked requests summary, and coverage assessment against OWASP Top 10.