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NeuroSploit v3.2.3 - Multi-Agent Security Testing Framework

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- Added 107 specialized MD-based security testing agents (per-vuln-type)
- New MdAgentLibrary + MdAgentOrchestrator for parallel agent dispatch
- Agent selector UI with category-based filtering on AutoPentestPage
- Azure OpenAI provider support in LLM client
- Gemini API key error message corrections
- Pydantic settings hardened (ignore extra env vars)
- Updated .gitignore for runtime data artifacts

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
This commit is contained in:
CyberSecurityUP
2026-03-16 18:59:22 -03:00
parent e5857d00c1
commit 7563260b2b
119 changed files with 6740 additions and 8 deletions
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backend/core/autonomous_agent.py
+119 -1
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@@ -171,6 +171,14 @@ except ImportError:
HAS_CLI_AGENT = False
CLIAgentRunner = None
# Phase 5.5: Markdown-based Agent Orchestration (post-recon agent dispatch)
try:
from backend.core.md_agent import MdAgentOrchestrator
HAS_MD_AGENTS = True
except ImportError:
HAS_MD_AGENTS = False
MdAgentOrchestrator = None
# Phase 6: Per-Vulnerability-Type Agent Orchestration
try:
from backend.core.vuln_orchestrator import VulnOrchestrator
@@ -350,10 +358,14 @@ class LLMClient:
def __init__(self, preferred_provider: Optional[str] = None, preferred_model: Optional[str] = None):
self.anthropic_key = os.getenv("ANTHROPIC_API_KEY", "")
self.openai_key = os.getenv("OPENAI_API_KEY", "")
self.google_key = os.getenv("GOOGLE_API_KEY", "") or os.getenv("GEMINI_API_KEY", "")
self.google_key = os.getenv("GEMINI_API_KEY", "") or os.getenv("GOOGLE_API_KEY", "")
self.together_key = os.getenv("TOGETHER_API_KEY", "")
self.fireworks_key = os.getenv("FIREWORKS_API_KEY", "")
self.openrouter_key = os.getenv("OPENROUTER_API_KEY", "")
self.azure_openai_key = os.getenv("AZURE_OPENAI_API_KEY", "")
self.azure_openai_endpoint = os.getenv("AZURE_OPENAI_ENDPOINT", "")
self.azure_openai_api_version = os.getenv("AZURE_OPENAI_API_VERSION", "2024-02-01")
self.azure_openai_deployment = os.getenv("AZURE_OPENAI_DEPLOYMENT", "")
self.codex_key = os.getenv("CODEX_API_KEY", "")
self.ollama_model = os.getenv("OLLAMA_MODEL", "llama3.2")
self.configured_model = os.getenv("DEFAULT_LLM_MODEL", "") # User-configured model name
@@ -399,6 +411,8 @@ class LLMClient:
self.openrouter_key = None
if self.codex_key in ["", "your-codex-api-key"]:
self.codex_key = None
if self.azure_openai_key in ["", "your-azure-openai-api-key"]:
self.azure_openai_key = None
# Try providers in order of preference
self._initialize_provider()
@@ -429,6 +443,22 @@ class LLMClient:
self.error_message = f"OpenAI init error: {e}"
print(f"[LLM] OpenAI initialization failed: {e}")
# 2a. Try Azure OpenAI
if OPENAI_AVAILABLE and self.azure_openai_key and self.azure_openai_endpoint:
try:
self.client = openai.AzureOpenAI(
api_key=self.azure_openai_key,
api_version=self.azure_openai_api_version,
azure_endpoint=self.azure_openai_endpoint,
)
self.provider = "azure_openai"
self.model_name = self.azure_openai_deployment or self.configured_model or "gpt-4o"
print(f"[LLM] Azure OpenAI initialized (deployment: {self.model_name})")
return
except Exception as e:
self.error_message = f"Azure OpenAI init error: {e}"
print(f"[LLM] Azure OpenAI initialization failed: {e}")
# 2b. Try Codex (OpenAI-compatible)
if OPENAI_AVAILABLE and self.codex_key:
try:
@@ -631,6 +661,17 @@ class LLMClient:
)
return response.choices[0].message.content
elif self.provider == "azure_openai":
response = self.client.chat.completions.create(
model=self.model_name or "gpt-4o",
max_tokens=max_tokens,
messages=[
{"role": "system", "content": system or default_system},
{"role": "user", "content": prompt}
]
)
return response.choices[0].message.content
elif self.provider == "gemini":
return await self._generate_gemini(prompt, system or default_system, max_tokens)
@@ -948,6 +989,7 @@ class AutonomousAgent:
methodology_file: Optional[str] = None,
enable_cli_agent: bool = False,
cli_agent_provider: Optional[str] = None,
selected_md_agents: Optional[List[str]] = None,
):
self.target = self._normalize_target(target)
self.mode = mode
@@ -966,6 +1008,7 @@ class AutonomousAgent:
self.preferred_model = preferred_model
self.enable_cli_agent = enable_cli_agent
self.cli_agent_provider = cli_agent_provider
self.selected_md_agents: Optional[List[str]] = selected_md_agents
self._cancelled = False
self._paused = False
self._skip_to_phase: Optional[str] = None # Phase skip target
@@ -1102,6 +1145,9 @@ class AutonomousAgent:
# Phase 5: Multi-agent orchestrator (optional replacement for 3-stream)
self._orchestrator = None # Lazy-init after session
# Phase 5.5: MD-based agent orchestrator (post-recon dispatch)
self._md_orchestrator = None # Lazy-init after session
# Researcher AI (0-day discovery with Kali sandbox, opt-in)
self._researcher = None # Lazy-init after session
@@ -3874,6 +3920,17 @@ NOT_VULNERABLE: <reason>"""
request_engine=self.request_engine,
)
# Phase 5.5: MD-based agent orchestrator (always available)
if HAS_MD_AGENTS:
self._md_orchestrator = MdAgentOrchestrator(
llm=self.llm,
memory=self.memory,
budget=self.token_budget,
validation_judge=self.validation_judge,
log_callback=self.log,
progress_callback=self.progress_callback,
)
# Researcher AI: 0-day discovery with Kali sandbox (opt-in)
researcher_enabled = (
HAS_RESEARCHER
@@ -4781,6 +4838,67 @@ NOT_VULNERABLE: <reason>"""
except Exception as e:
await self.log("debug", f" [CHAIN] AI discovery error: {e}")
# ── MD-BASED AGENT DISPATCH (post-recon specialist agents) ──
if self._md_orchestrator and not self.is_cancelled():
try:
await self.log("info", "[MD-AGENTS] Dispatching specialist .md agents with recon context")
md_result = await self._md_orchestrator.run(
target=self.target,
recon_data=self.recon,
existing_findings=self.findings,
selected_agents=self.selected_md_agents,
headers=dict(self.auth_headers),
waf_info=(
self._waf_result.get("waf_name", "")
if self._waf_result else ""
),
)
# Merge MD agent findings into main findings via validation
md_findings_raw = md_result.get("findings", [])
md_confirmed = 0
for mf in md_findings_raw:
if self.is_cancelled():
break
if not isinstance(mf, dict):
continue
try:
finding = Finding(
id=str(hashlib.md5(
f"{mf.get('title', '')}{mf.get('affected_endpoint', '')}".encode()
).hexdigest())[:12],
title=mf.get("title", "MD Agent Finding"),
severity=mf.get("severity", "medium"),
vulnerability_type=mf.get("vulnerability_type", "unknown"),
cvss_score=mf.get("cvss_score", 0.0),
cwe_id=mf.get("cwe_id", ""),
description=mf.get("description", ""),
affected_endpoint=mf.get("affected_endpoint", self.target),
evidence=mf.get("evidence", ""),
poc_code=mf.get("poc_code", ""),
impact=mf.get("impact", ""),
remediation=mf.get("remediation", ""),
confidence_score=50,
confidence="medium",
ai_verified=False,
ai_status="pending",
)
# Flow through validation pipeline
await self._add_finding(finding)
md_confirmed += 1
except Exception as e:
await self.log("debug", f" [MD-AGENTS] Finding merge error: {e}")
agent_summary = md_result.get("agent_results", {})
agents_run = md_result.get("agents_run", 0)
await self.log("info",
f"[MD-AGENTS] Complete: {agents_run} agents, "
f"{len(md_findings_raw)} raw findings, "
f"{md_confirmed} submitted to validation, "
f"{md_result.get('duration', 0)}s")
except Exception as e:
await self.log("warning", f"[MD-AGENTS] Dispatch error: {e}")
# ── RESEARCHER AI (0-day discovery with Kali sandbox) ──
if self._researcher and not self.is_cancelled():
try: