feat: redmesh deepseek integration

extensions/business/cybersec/red_mesh/constants.py

@@ -96,4 +96,14 @@
 
 # Port order constants
 PORT_ORDER_SHUFFLE = "SHUFFLE"
-PORT_ORDER_SEQUENTIAL = "SEQUENTIAL"
+PORT_ORDER_SEQUENTIAL = "SEQUENTIAL"
+
+# LLM Agent API status constants
+LLM_API_STATUS_OK = "ok"
+LLM_API_STATUS_ERROR = "error"
+LLM_API_STATUS_TIMEOUT = "timeout"
+
+# LLM Analysis types
+LLM_ANALYSIS_SECURITY_ASSESSMENT = "security_assessment"
+LLM_ANALYSIS_VULNERABILITY_SUMMARY = "vulnerability_summary"
+LLM_ANALYSIS_REMEDIATION_PLAN = "remediation_plan"

extensions/business/cybersec/red_mesh/pentester_api_01.py

@@ -34,12 +34,19 @@
 
 from naeural_core.business.default.web_app.fast_api_web_app import FastApiWebAppPlugin as BasePlugin
 from .redmesh_utils import PentestLocalWorker  # Import PentestJob from separate module
-from .constants import FEATURE_CATALOG
+from .redmesh_llm_agent_mixin import _RedMeshLlmAgentMixin
+from .constants import (
+  FEATURE_CATALOG,
+  LLM_ANALYSIS_SECURITY_ASSESSMENT,
+  LLM_ANALYSIS_VULNERABILITY_SUMMARY,
+  LLM_ANALYSIS_REMEDIATION_PLAN,
+)
 
 __VER__ = '0.8.2' 
 
 _CONFIG = {
   **BasePlugin.CONFIG,
+
   "TUNNEL_ENGINE_ENABLED": False,
 
   'PORT': None,
@@ -49,9 +56,9 @@
   "CHECK_JOBS_EACH" : 5,
 
   "REDMESH_VERBOSE" : 10,  # Verbosity level for debug messages (0 = off, 1+ = debug)
-  
+
   "NR_LOCAL_WORKERS" : 8,
-  
+
   "WARMUP_DELAY" : 30,
 
   # Defines how ports are split across local workers.
@@ -68,12 +75,19 @@
   "SCAN_MIN_RND_DELAY": 0.0,  # minimum delay in seconds (0 = disabled)
   "SCAN_MAX_RND_DELAY": 0.0,  # maximum delay in seconds (0 = disabled)
 
+  # LLM Agent API integration for auto-analysis
+  "LLM_AGENT_API_ENABLED": False,  # Enable LLM-powered analysis
+  "LLM_AGENT_API_HOST": "127.0.0.1",  # Host where LLM Agent API is running
+  "LLM_AGENT_API_PORT": None,  # Port for LLM Agent API (required if enabled)
+  "LLM_AGENT_API_TIMEOUT": 120,  # Timeout in seconds for LLM API calls
+  "LLM_AUTO_ANALYSIS_TYPE": "security_assessment",  # Default analysis type
+
   'VALIDATION_RULES': {
-    **BasePlugin.CONFIG['VALIDATION_RULES'],  
+    **BasePlugin.CONFIG['VALIDATION_RULES'],
   },
 }
 
-class PentesterApi01Plugin(BasePlugin):
+class PentesterApi01Plugin(BasePlugin, _RedMeshLlmAgentMixin):
   """
   RedMesh API plugin for orchestrating decentralized pentest jobs.
 
@@ -846,6 +860,11 @@ def _maybe_finalize_pass(self):
           job_specs["date_updated"] = self.time()
           job_specs["date_finalized"] = self.time()
           self.P(f"[SINGLEPASS] Job {job_id} complete. Status set to FINALIZED.")
+
+          # Run LLM auto-analysis on aggregated report (launcher only)
+          if self.cfg_llm_agent_api_enabled:
+            self._run_aggregated_llm_analysis(job_id, job_specs, workers, pass_nr=job_pass)
+
           self.chainstore_hset(hkey=self.cfg_instance_id, key=job_key, value=job_specs)
           continue
 
@@ -857,10 +876,19 @@ def _maybe_finalize_pass(self):
           job_specs["date_updated"] = self.time()
           job_specs["date_finalized"] = self.time()
           self.P(f"[CONTINUOUS] Pass {job_pass} complete for job {job_id}. Status set to STOPPED (soft stop was scheduled)")
+
+          # Run LLM auto-analysis on aggregated report (launcher only)
+          if self.cfg_llm_agent_api_enabled:
+            self._run_aggregated_llm_analysis(job_id, job_specs, workers, pass_nr=job_pass)
+
           self.chainstore_hset(hkey=self.cfg_instance_id, key=job_key, value=job_specs)
           continue
         # end if
 
+        # Run LLM auto-analysis for this pass (launcher only)
+        if self.cfg_llm_agent_api_enabled:
+          self._run_aggregated_llm_analysis(job_id, job_specs, workers, pass_nr=job_pass)
+
         # Schedule next pass
         interval = job_specs.get("monitor_interval", self.cfg_monitor_interval)
         jitter = random.uniform(0, self.cfg_monitor_jitter)
@@ -1487,6 +1515,230 @@ def stop_monitoring(self, job_id: str, stop_type: str = "SOFT"):
     }
 
 
+  @BasePlugin.endpoint(method="post")
+  def analyze_job(
+      self,
+      job_id: str,
+      analysis_type: str = "",
+      focus_areas: list[str] = None
+  ):
+    """
+    Manually trigger LLM analysis for a completed job.
+
+    Aggregates reports from all workers and runs analysis on the combined data.
+
+    Parameters
+    ----------
+    job_id : str
+      Identifier of the job to analyze.
+    analysis_type : str, optional
+      Type of analysis: "security_assessment", "vulnerability_summary", "remediation_plan".
+    focus_areas : list[str], optional
+      Areas to focus on: ["web", "network", "databases", "authentication"].
+
+    Returns
+    -------
+    dict
+      LLM analysis result or error message.
+    """
+    if not self.cfg_llm_agent_api_enabled:
+      return {"error": "LLM Agent API is not enabled", "job_id": job_id}
+
+    if not self.cfg_llm_agent_api_port:
+      return {"error": "LLM Agent API port not configured", "job_id": job_id}
+
+    # Get job from CStore
+    job_specs = self._get_job_from_cstore(job_id)
+    if not job_specs:
+      return {"error": "Job not found", "job_id": job_id}
+
+    workers = job_specs.get("workers", {})
+    if not workers:
+      return {"error": "No workers found for this job", "job_id": job_id}
+
+    # Check if all workers have finished
+    all_finished = all(w.get("finished") for w in workers.values())
+    if not all_finished:
+      return {"error": "Job not yet complete, some workers still running", "job_id": job_id}
+
+    # Collect and aggregate reports from all workers
+    aggregated_report = self._collect_aggregated_report(workers)
+
+    if not aggregated_report:
+      return {"error": "No report data available for this job", "job_id": job_id}
+
+    # Add job metadata to report for context
+    target = job_specs.get("target", "unknown")
+    aggregated_report["_job_metadata"] = {
+      "job_id": job_id,
+      "target": target,
+      "num_workers": len(workers),
+      "worker_addresses": list(workers.keys()),
+      "start_port": job_specs.get("start_port"),
+      "end_port": job_specs.get("end_port"),
+      "enabled_features": job_specs.get("enabled_features", []),
+    }
+
+    # Call LLM Agent API
+    analysis_type = analysis_type or self.cfg_llm_auto_analysis_type
+
+    analysis_result = self._call_llm_agent_api(
+      endpoint="/analyze_scan",
+      method="POST",
+      payload={
+        "scan_results": aggregated_report,
+        "analysis_type": analysis_type,
+        "focus_areas": focus_areas,
+      }
+    )
+
+    if "error" in analysis_result:
+      return {
+        "error": analysis_result.get("error"),
+        "status": analysis_result.get("status"),
+        "job_id": job_id,
+      }
+
+    # Save analysis to R1FS and store in pass_history
+    analysis_cid = None
+    pass_history = job_specs.get("pass_history", [])
+    current_pass = job_specs.get("job_pass", 1)
+
+    try:
+      analysis_cid = self.r1fs.add_json(analysis_result, show_logs=False)
+      if analysis_cid:
+        # Store in pass_history (find the latest completed pass)
+        if pass_history:
+          # Update the latest pass entry with analysis CID
+          pass_history[-1]["llm_analysis_cid"] = analysis_cid
+        else:
+          # No pass_history yet - create one
+          pass_history.append({
+            "pass_nr": current_pass,
+            "completed_at": self.time(),
+            "reports": {addr: w.get("report_cid") for addr, w in workers.items()},
+            "llm_analysis_cid": analysis_cid,
+          })
+          job_specs["pass_history"] = pass_history
+
+        self.chainstore_hset(hkey=self.cfg_instance_id, key=job_id, value=job_specs)
+        self.P(f"Manual LLM analysis saved for job {job_id}, CID: {analysis_cid}")
+    except Exception as e:
+      self.P(f"Failed to save analysis to R1FS: {e}", color='y')
+
+    return {
+      "job_id": job_id,
+      "target": target,
+      "num_workers": len(workers),
+      "pass_nr": pass_history[-1].get("pass_nr") if pass_history else current_pass,
+      "analysis_type": analysis_type,
+      "analysis": analysis_result,
+      "analysis_cid": analysis_cid,
+    }
+
+
+  @BasePlugin.endpoint
+  def get_analysis(self, job_id: str = "", cid: str = "", pass_nr: int = None):
+    """
+    Retrieve LLM analysis for a job or by CID.
+
+    The analysis is generated by the launcher node after all workers complete,
+    containing the aggregated results from all distributed workers.
+
+    Parameters
+    ----------
+    job_id : str, optional
+      Identifier of the job to get analysis for.
+    cid : str, optional
+      Direct CID of the analysis to retrieve.
+    pass_nr : int, optional
+      Pass number for continuous jobs. If not provided, returns the latest pass.
+
+    Returns
+    -------
+    dict
+      LLM analysis data or error message.
+    """
+    # If CID provided directly, fetch it
+    if cid:
+      try:
+        analysis = self.r1fs.get_json(cid)
+        if analysis is None:
+          return {"error": "Analysis not found", "cid": cid}
+        return {"cid": cid, "analysis": analysis}
+      except Exception as e:
+        return {"error": str(e), "cid": cid}
+
+    # Otherwise, look up by job_id
+    if not job_id:
+      return {"error": "Either job_id or cid must be provided"}
+
+    job_specs = self._get_job_from_cstore(job_id)
+    if not job_specs:
+      return {"error": "Job not found", "job_id": job_id}
+
+    # Look for analysis in pass_history
+    pass_history = job_specs.get("pass_history", [])
+    job_status = job_specs.get("job_status", "RUNNING")
+
+    if not pass_history:
+      if job_status == "RUNNING":
+        return {"error": "Job still running, no passes completed yet", "job_id": job_id, "job_status": job_status}
+      return {"error": "No pass history available for this job", "job_id": job_id, "job_status": job_status}
+
+    # Find the requested pass (or latest if not specified)
+    target_pass = None
+    if pass_nr is not None:
+      for entry in pass_history:
+        if entry.get("pass_nr") == pass_nr:
+          target_pass = entry
+          break
+      if not target_pass:
+        return {"error": f"Pass {pass_nr} not found in history", "job_id": job_id, "available_passes": [e.get("pass_nr") for e in pass_history]}
+    else:
+      # Get the latest pass
+      target_pass = pass_history[-1]
+
+    analysis_cid = target_pass.get("llm_analysis_cid")
+    if not analysis_cid:
+      return {
+        "error": "No LLM analysis available for this pass",
+        "job_id": job_id,
+        "pass_nr": target_pass.get("pass_nr"),
+        "job_status": job_status
+      }
+
+    try:
+      analysis = self.r1fs.get_json(analysis_cid)
+      if analysis is None:
+        return {"error": "Analysis not found in R1FS", "cid": analysis_cid, "job_id": job_id}
+      return {
+        "job_id": job_id,
+        "pass_nr": target_pass.get("pass_nr"),
+        "completed_at": target_pass.get("completed_at"),
+        "cid": analysis_cid,
+        "target": job_specs.get("target"),
+        "num_workers": len(job_specs.get("workers", {})),
+        "total_passes": len(pass_history),
+        "analysis": analysis,
+      }
+    except Exception as e:
+      return {"error": str(e), "cid": analysis_cid, "job_id": job_id}
+
+
+  @BasePlugin.endpoint
+  def llm_health(self):
+    """
+    Check health of the LLM Agent API connection.
+
+    Returns
+    -------
+    dict
+      Health status of the LLM Agent API.
+    """
+    return self._get_llm_health_status()
+
+
   def process(self):
     """
     Periodic task handler: launch new jobs and close completed ones.