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Introduction:
The convergence of artificial intelligence and cybersecurity is rapidly shifting from theoretical discussions to practical, code‑driven implementations. A recent development in this space is “nova,” an agent development kit designed to orchestrate micro‑teams of AI agents through a unified definition of Team objects. This framework introduces a structured workflow comprising a planner, runner, synthesizer, and reporter, enabling security professionals to automate complex, multi‑step tasks such as penetration testing, threat hunting, and incident response with unprecedented precision and scalability.
Learning Objectives:
- Understand the architecture of the nova agent development kit and its micro‑teams workflow.
- Learn to define and deploy AI agents that can plan, execute, synthesize, and report on security tasks.
- Acquire hands‑on skills in integrating nova with existing security tools and infrastructure using practical commands and configurations.
You Should Know:
- Deploying Nova and Defining Your First Agent Team
The core philosophy behind nova is to treat AI agents as composable units that function as a coordinated team. The framework requires a single definition file where you specify the roles—planner, runner, synthesizer, reporter—and the corresponding objectives. This approach mirrors modern DevOps practices, allowing for version control, repeatability, and collaborative development. To begin, clone the repository and set up a virtual environment:
Clone the nova repository git clone https://github.com/trustedsec/nova.git cd nova Create and activate a Python virtual environment python3 -m venv venv source venv/bin/activate On Windows: venv\Scripts\activate Install dependencies pip install -r requirements.txt
Once installed, create a `team_definition.yaml` file. This file outlines the team’s mission, the agents, and their specific tools. For example, a red team micro‑team might consist of a planner to map attack vectors, a runner to execute exploits, a synthesizer to correlate results, and a reporter to document findings.
- Configuring the Planner and Runner for Active Reconnaissance
The planner agent uses a large language model (LLM) to break down a high‑level goal (e.g., “Enumerate subdomains and identify vulnerable services”) into a sequence of actionable steps. The runner agent then executes these steps, often interfacing with external tools. Below is an example of a runner configuration that leverages common reconnaissance utilities:
planner: model: gpt-4 prompt: "Create a step-by-step plan to enumerate subdomains and scan for open ports on target.com" runner: tools: - name: subfinder command: "subfinder -d target.com -o subdomains.txt" - name: nmap command: "nmap -iL subdomains.txt -p- --open -oN nmap_scan.txt"
To execute the plan, use the nova CLI:
nova run --definition team_definition.yaml --target target.com
This command triggers the planner to generate a task list, which the runner executes sequentially, storing outputs for the next phase.
3. Synthesizing Data and Generating Reports
After the runner completes its tasks, the synthesizer agent ingests the raw output files, correlates them, and produces structured intelligence. For instance, it can identify patterns such as multiple hosts running outdated Apache versions or open SMB ports that might indicate a potential EternalBlue vulnerability. The reporter agent then formats this intelligence into a professional report. Here’s a snippet from a synthesizer configuration:
synthesizer: model: gpt-4 input_files: - subdomains.txt - nmap_scan.txt output_file: synthesized_findings.json prompt: "Analyze the scan results and list any services with known CVEs, prioritizing by severity."
You can extend this by integrating with APIs like the National Vulnerability Database (NVD) to enrich findings:
Custom synthesizer script example
import requests
import json
with open('nmap_scan.txt', 'r') as f:
services = parse_nmap_output(f)
for service in services:
cve_query = f"https://services.nvd.nist.gov/rest/json/cves/2.0?keywordSearch={service['name']}"
response = requests.get(cve_query)
service['cves'] = response.json().get('vulnerabilities', [])
4. Automating Exploitation with Runner‑Tool Integration
The runner agent can be extended to trigger exploitation frameworks like Metasploit or custom scripts. To safely demonstrate, a runner can be configured to run a harmless test—such as checking for default credentials—by invoking a Python script:
runner: tools: - name: check_default_creds command: "python3 check_creds.py --hosts subdomains.txt --user admin --password admin"
For Windows environments, you might integrate PowerShell modules:
Example runner command for Windows Invoke-Expression "& 'C:\Tools\Invoke-Heartbleed.ps1' -Targets (Get-Content subdomains.txt)"
All runner activities are logged, allowing the synthesizer to later filter successes and failures.
5. Hardening Cloud Environments Using Nova’s Feedback Loop
Beyond offensive security, nova can be deployed in defensive roles to continuously validate security controls. For example, a micro‑team can be tasked with verifying cloud IAM policies. The planner defines checks for over‑privileged roles, the runner calls AWS CLI commands, the synthesizer compares them against best practices, and the reporter outputs a compliance dashboard. This creates a continuous auditing loop that can be scheduled with cron jobs or CI/CD pipelines:
Schedule a daily compliance check using cron 0 2 /path/to/nova run --definition cloud_compliance.yaml --target aws_account_id >> /var/log/nova_audit.log 2>&1
For cloud hardening, a typical runner command might be:
aws iam list-roles --query "Roles[?AssumeRolePolicyDocument.Statement[?Effect=='Allow' && Condition.NumericLessThanEquals]]" --output json > over_privileged_roles.json
The synthesizer can then flag any role with “ in its resource field as a potential risk.
What Undercode Say:
- Key Takeaway 1: The nova framework successfully demonstrates how AI agents can be structured into micro‑teams to automate complex cybersecurity workflows, bridging the gap between human strategic thinking and machine‑speed execution.
- Key Takeaway 2: By separating the planning, execution, synthesis, and reporting phases, nova enables continuous improvement and transparency—each step’s output can be reviewed, version‑controlled, and optimized independently.
This architecture mirrors the shift left movement in security, embedding intelligent automation into the earliest stages of reconnaissance and testing. For practitioners, adopting such frameworks reduces manual overhead, ensures consistency in testing methodologies, and accelerates the detection of critical vulnerabilities. However, organizations must remain cautious about the security of the AI models themselves, ensuring that sensitive data is not inadvertently exposed to third‑party LLM providers. As these toolkits mature, we can expect a new class of “AI security engineers” who specialize in orchestrating these agent teams, effectively making advanced security testing accessible to smaller teams and even solo practitioners.
Prediction:
In the next two years, AI‑driven micro‑teams like those built with nova will become standard components in both red and blue team operations. We will see the rise of self‑healing infrastructures where agent teams not only detect misconfigurations but autonomously deploy remediation patches, all while maintaining a full audit trail. This will shift the cybersecurity workforce towards roles focused on orchestrating, training, and auditing these AI agents, rather than performing repetitive tasks manually. The integration of such frameworks with continuous integration and continuous deployment (CI/CD) pipelines will make security a native, automated layer in software development lifecycles, reducing the mean time to remediation (MTTR) from days to minutes.
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