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Introduction:
In the relentless focus on securing firewalls and patching servers, a critical attack vector often goes unnoticed: the corporate messaging platform. Tools like Microsoft Teams, designed for collaboration, have become convenient but insecure repositories for passwords, API keys, and database credentials. This article explores HadesHunter, an open-source tool that automates the discovery of these secrets, turning a compromised user account into a catastrophic data breach.
Learning Objectives:
- Understand how HadesHunter extracts and analyzes Microsoft Teams conversation history for exposed secrets.
- Learn the step-by-step process to deploy and configure HadesHunter for security auditing and red team exercises.
- Implement defensive strategies and policies to mitigate the risk of secret exposure in collaborative communications.
You Should Know:
- The Prerequisites: Gaining a Foothold for the Hunt
Before HadesHunter can scan, it needs access. The tool operates under the assumption that an attacker has already compromised a user’s credentials or that a security team is conducting an authorized audit with proper consent.
Step-by-step guide explaining what this does and how to use it.
Objective: Obtain the necessary Microsoft Teams authentication tokens.
For a Red Team / Simulated Attack: This often involves phishing to steal a user’s session cookies or using a tool like `Snaffler` to find cached credentials on a compromised endpoint. Legal Note: This must only be performed in authorized penetration tests with explicit written scope.
For a Defensive Audit: Use a dedicated audit account with the necessary `User.Read.All` and `Chat.Read.All` Microsoft Graph API permissions. You can generate tokens using the Azure Portal.
Command to Verify Access (Using Graph API):
Use curl with a Bearer token to test permissions and list accessible chats curl -H "Authorization: Bearer YOUR_ACCESS_TOKEN" \ -H "Content-Type: application/json" \ https://graph.microsoft.com/v1.0/me/chats
A successful call returns a JSON list of chats, confirming the tool has the data access it needs.
2. Installation & Setup: Deploying HadesHunter
HadesHunter is a Python-based tool, making it cross-platform. Its setup is straightforward but requires careful dependency management.
Step-by-step guide explaining what this does and how to use it.
Step 1: Clone the Repository. Open a terminal (Linux/macOS) or PowerShell/CMD (Windows) and clone the tool from its GitHub repository.
git clone https://github.com/anak0ndah/HadesHunter.git cd HadesHunter
Step 2: Install Python Dependencies. The tool relies on libraries like `requests` for API calls and `regex` for pattern matching. Use `pip` to install them.
Linux/macOS, use python3 and pip3 python3 -m pip install -r requirements.txt Windows, ensure Python is in your PATH pip install -r requirements.txt
Step 3: Configure Your Token. The tool expects your Microsoft Graph API access token. You can pass it directly via command line (shown next) or, for less secure testing, hardcode it in the script’s `config.py` if it exists. Best practice is to use command-line arguments or environment variables.
3. Execution & Scanning: Unleashing the Hunter
With setup complete, you can execute the core function of HadesHunter: fetching conversation history and scanning it for secrets.
Step-by-step guide explaining what this does and how to use it.
Step 1: Basic Scan. Run the main script with your access token. The `-t` flag is for the token.
Basic syntax for both Linux and Windows python3 hadeshunter.py -t "YOUR_LONG_ACCESS_TOKEN_HERE" On Windows, if python3 doesn't work, try 'python' python hadeshunter.py -t "YOUR_LONG_ACCESS_TOKEN_HERE"
Step 2: Understand the Output. The tool will start fetching chats and messages. It uses regular expressions (regex) to detect patterns like AWS keys (AKIA[0-9A-Z]{16}), GitHub tokens (ghp_[a-zA-Z0-9]{36}), and generic passwords. Each finding is printed to the console with context.
Step 3: Targeted Scans (If Supported). Advanced usage might involve scanning only specific chats or for specific secret types. Check the tool’s help menu for evolving features.
python3 hadeshunter.py --help
4. Analysis of Results: From Data to Intelligence
The raw output is just the first step. A true security assessment requires analyzing these findings to gauge risk and impact.
Step-by-step guide explaining what this does and how to use it.
Step 1: Triage Findings. Categorize discovered secrets:
Critical: Cloud root keys, database connection strings with admin privileges.
High: API keys for payment processors (Stripe), code repositories (GitHub), communication tools (Slack).
Medium/Low: Internal test credentials, expired tokens.
Step 2: Establish Context. Where was the secret shared? A private 1:1 chat limits exposure. A message in a public team with 100 members signifies a major incident.
Step 3: Document for Reporting. For audits or incident response, create a structured report: Secret Type, Where Found, Date Shared, Potential Impact, and Immediate Action (e.g., Key Rotation).
5. Defensive Mitigation: Building Your Shield
The real value of HadesHunter is proactive defense. Use its findings to build stronger controls.
Step-by-step guide explaining what this does and how to use it.
Step 1: Implement Technical Controls.
Deploy a Data Loss Prevention (DLP) tool for Microsoft 365. Configure it to detect and block messages containing patterns of API keys or passwords before they are sent.
Use Microsoft Purview Insider Risk Management policies. Create indicators to detect the sharing of sensitive information in Teams and generate alerts.
Step 2: Enforce Policy & Training.
Establish and communicate a clear “No Secrets in Chat” policy. Mandate the use of dedicated secret managers (Azure Key Vault, AWS Secrets Manager, HashiCorp Vault).
Conduct regular security awareness training with specific examples of what not to share in Teams, using anonymized findings from your own scans.
Step 3: Proactive Hunting. Integrate authorized scanning with HadesHunter into your continuous security monitoring or compromise assessment routines to regularly check for policy violations.
What Undercode Say:
Key Takeaway 1: The tool highlights a pervasive cultural-technical gap. Security teams often secure the perimeter but neglect the “human middleware”—the channels where convenience routinely overrides policy, creating a soft, high-value interior target.
Key Takeaway 2: HadesHunter is a dual-purpose instrument. In the wrong hands, it’s a potent post-exploitation tool. In the right hands, it’s an essential audit and awareness driver that provides tangible evidence to convince management and users of a real, neglected threat.
Analysis:
HadesHunter succeeds by automating a tedious but critical process, making the invisible, visible. Its value lies not in technical novelty, but in applying a classic security concept—credential hunting—to a modern data source that falls between IT and security team responsibilities. The tool’s true impact is measured by the organizational changes it provokes: the adoption of secret managers, the tuning of DLP policies, and the cultural shift it can help initiate. It serves as a stark reminder that in a platform-as-a-service world, attackers will always target the path of least resistance, which is increasingly the unmonitored chatlog, not the fortified firewall.
Prediction:
The success of tools like HadesHunter will catalyze two major trends. First, we will see a rapid integration of native, advanced secret-scanning capabilities directly within collaborative platforms like Teams and Slack, similar to how GitHub now scans repositories. Second, this vector will become a primary focus for Initial Access Brokers (IABs) in the cybercriminal ecosystem. Instead of just selling RDP access to a server, they will monetize compromised corporate user accounts specifically for their rich chat history, creating a new market for “credential-rich, identity-based access.” This will force a fundamental shift in defense strategies from pure network segmentation to deep, behavior-based identity and data governance.
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IT/Security Reporter URL:
Reported By: Laurent Minne – Hackers Feeds
Extra Hub: Undercode MoN
Basic Verification: Pass ✅
