Apr 11 2023

Threat Modeling with ChatGPT4

I was curious how ChatGPT (using the GPT4 model) would do with threat modeling a system design presented by a theoretical developer. The following has not been edited, except for formatting, and represents a very impressive performance for a chat model that is not tuned for security work in general, or threat modeling in particuler. To make things more complex I was intentionally a bit vague in my responses to its design questions and I added a couple questions in the middle and end of my responses to ChatGPT. Impressively it even made sure to include answers to those questions in its final response. Note, a large part of the description of STRIDE was adapted from here: https://threat-modeling.com/stride-threat-modeling/

The Opening Prompt

“You are a security engineer that is helping a programmer at your company to threat model a product they are designing. You need to identify system components, cybersecurity threats, and propose countermeasures for their system design. Each threat should be in one of the threat categories from the STRIDE threat modeling framework.

The STRIDE framework is used to map out your application based on it’s unique use cases and business logic. STRIDE t can be used to identify and potential vulnerabilities during system design.

The structure of a component, threat, and countermeasures (or mitigations / security requirements) can vary, but will look something like this:

  • Component X (which is a component within the scope of our threat modeling)
    • Threat X: description of the threat
      • Countermeasure 1: description of countermeasure 1
      • Countermeasure 2: description of countermeasure 2
    • Threat Y: description of the threat
      • Countermeasure 1: description of countermeasure 3
      • Countermeasure 2: description of countermeasure 4

STRIDE threat modeling is a specific kind of threat modeling methodology (or method). It is a mnemonic of six types of security threats. Each letter of STRIDE stands for one of the six types of security threats:

  1. Spoofing
  2. Tampering
  3. Repudiation
  4. Information Disclosure
  5. Denial of Service
  6. Elevation of Privilege

Some considerations when defining countermeasures:

  • Countermeasures do not have to be technology-based, they can also be covered by people or processes too (or even a combination of people, process, and technology).
  • You can define multiple countermeasures for one threat if a countermeasure does not fully cover the impact or risk of the threat.

Spoofing - Spoofing is a type of threat whereby an attacker maliciously impersonates (or pretends to be) a different user (or system). You can also use Spoofing more loosely during STRIDE threat modeling to classify threats related to users and access rights.

Tampering - Tampering is a type of threat whereby an attacker maliciously modifies data. You can also use Tampering more loosely during STRIDE threat modeling to classify threats related to the security of data.

Repudiation - Repudiation relates to the ability to prove or disprove that an action or activity was performed by a specific user (or not). Repudiation is thus a type of threat whereby an attacker denies having performed a malicious action.

Information Disclosure - Information Disclosure is a type of threat whereby the attacker gains access to information that should be confidential or secret (and not available to an attacker).

Denial of Service - Denial of Service is a type of threat whereby an attacker will prevent a system (or application) from working for valid users. This is often achieved by overloading a system with fake requests so that no time or resources remain for legitimate users.

Elevation of Privilege - Elevation of Privilege is a type of threat whereby an attacker will elevate their current level of access privilege. This can include elevating access privileges where an attacker has no privileges at all (i.e., not a user) or elevating access privileges where an attacker already has ‘some’ privileges (i.e., a basic user).

Programmer: I want to build a system in AWS that aggregates syslogs from 1000 servers, merges the logs, and encrypts them. What follow-up questions do you have about the system design, before we get started on threat modeling?”

Questions from ChatGPT4

“Before we get started on threat modeling, I have a few follow-up questions about the system design:

  1. What kind of servers are these 1000 servers? Are they all within the same network, or are they spread across different networks or environments (e.g., on-premises, cloud, or hybrid)?
  2. How will the syslogs be transmitted from the servers to the central aggregation point? Will there be any intermediary systems or services involved in the process?
  3. How will the merged logs be stored, and for how long? Will they be stored in a database, file storage, or another type of storage solution?
  4. What kind of encryption will be used for the logs? Will the encryption be symmetric or asymmetric? Will the encryption keys be managed internally or by a third-party service?
  5. Who will have access to the aggregated and encrypted logs? How will access be granted and managed, and will there be different levels of access?
  6. How will the system authenticate and authorize the servers sending the syslogs?
  7. Are there any compliance requirements or industry standards that the system needs to adhere to, such as GDPR, HIPAA, or PCI-DSS?
  8. Will there be any monitoring or alerting mechanisms in place to detect potential security incidents or anomalies in the logging system?
  9. Will the system have any integration with other systems or services, such as Security Information and Event Management (SIEM) or Incident Response platforms?”

Answers from the Application Developer

I tried to provide answers inline with how I’d expect an Application Developer to answer over email or in a ticket.

  1. The servers are all EC2 machines running Amazon Linux 2. They are spread across a variety of AWS accounts and VPCs within each account.
  2. The syslogs will be transmitted using a custom aggregating script that compresses the logs and uploads them to an S3 bucket.
  3. The merged logs need to be stored in S3 for 10 years.
  4. I don’t know what kind of encryption to use, what do you recommend?
  5. Access will be granted to application developers and the site reliability engineering team. Access will be managed through AWS IAM user roles.
  6. The log upload script on the servers will upload the logs with AWS Access Keys.
  7. GDPR compliance.
  8. No, should there be?

Threat Model from ChatGPT4

“Based on your answers, I’ll now provide a threat model for your system using the STRIDE framework:

Component: EC2 instances

Threat: Spoofing (impersonation of EC2 instances)

  1. Countermeasure 1: Use IAM instance profiles with proper roles and permissions for each instance to securely access AWS services.
  2. Countermeasure 2: Rotate AWS Access Keys regularly to limit the impact of compromised credentials.

Threat: Tampering (unauthorized modification of syslogs)

  1. Countermeasure: Enable integrity checking mechanisms, such as digital signatures, to ensure the logs haven’t been altered before being aggregated.

Component: Custom aggregating script

Threat: Tampering (unauthorized modification of the script)

  1. Countermeasure 1: Store the script in a secure, version-controlled repository with access controls.
  2. Countermeasure 2: Implement code signing to verify the script’s integrity before execution.

Component: S3 bucket

Threat: Information Disclosure (unauthorized access to stored logs)

  1. Countermeasure 1: Use server-side encryption with either Amazon S3-managed keys (SSE-S3) or AWS Key Management Service (KMS) keys (SSE-KMS) to encrypt the logs.
  2. Countermeasure 2: Implement proper access controls using IAM policies and bucket policies to restrict access to authorized users only.

Threat: Denial of Service (disruption of the log storage service)

  1. Countermeasure: Enable S3 bucket versioning to protect against accidental deletion or overwriting of logs.

Component: Access management

Threat: Elevation of Privilege (unauthorized access to log data)

  1. Countermeasure 1: Implement the principle of least privilege for IAM roles, granting the minimum necessary permissions to users.
  2. Countermeasure 2: Use IAM role-based access control for the site reliability engineering team and application developers, with separate roles for different tasks.

Regarding monitoring and alerting mechanisms, it’s a good practice to have them in place to detect potential security incidents or anomalies. Consider implementing the following:

  1. Enable AWS CloudTrail to monitor and log API calls to your AWS services, and use Amazon GuardDuty for threat detection.
  2. Set up monitoring and alerting for unusual access patterns or activities within your S3 bucket, such as sudden spikes in access requests or unauthorized access attempts.
  3. Use Amazon CloudWatch Logs and AWS Config to monitor changes to your AWS resources and configurations.
  4. Implement a centralized logging and monitoring solution, such as a SIEM platform, to analyze the logs and identify any potential security incidents or vulnerabilities.

For encryption, I recommend using server-side encryption with AWS KMS keys (SSE-KMS) because it provides better control and auditability over the encryption keys. This will help you comply with GDPR requirements by ensuring that your logs are encrypted at rest and only accessible by authorized users.”

Me: “can you make a data flow diagram for this system?”

As a text-based AI, I’m unable to create visual diagrams directly. However, I can provide you with a description of the data flow for your system, which you can use to create a Data Flow Diagram (DFD) using your preferred diagramming tool.

  1. Syslogs are generated by the EC2 instances across multiple AWS accounts and VPCs.
  2. The custom aggregating script running on each EC2 instance compresses and collects the syslogs.
  3. The script authenticates with AWS using IAM instance profiles or AWS Access Keys.
  4. The compressed syslogs are uploaded to an S3 bucket. The S3 bucket should have server-side encryption enabled with AWS KMS keys (SSE-KMS).
  5. Authorized users (application developers and site reliability engineering team members) access the logs in the S3 bucket through AWS Management Console or API using their assigned IAM roles.
  6. When users access the logs, the logs are decrypted automatically by the AWS service using the appropriate KMS key.

To create a DFD, represent each component (EC2 instances, custom aggregating script, S3 bucket, and authorized users) as separate entities. Then, use arrows to show the flow of data between these entities according to the steps described above. Make sure to label each arrow with a brief description of the data flow (e.g., “Compressed syslogs” or “Access logs”).

Me: “keep going”

Below, I’ll provide some additional steps that may be relevant to your system, as well as further details that could be helpful in understanding the data flow:

  1. Periodically, the logs stored in the S3 bucket are merged, possibly using an AWS Lambda function or an EC2 instance with a scheduled task.
  2. After merging the logs, the merged logs are stored back in the S3 bucket, still encrypted with SSE-KMS.
  3. To comply with GDPR, consider implementing a mechanism to identify and remove personal data from the logs, either during the aggregation process or as a separate step. This could involve using AWS Lambda or another data processing service.
  4. As part of your security monitoring efforts, configure AWS CloudTrail to log API calls made within your AWS environment. Integrate Amazon GuardDuty for threat detection and send CloudTrail logs to a separate S3 bucket with the same encryption and access control measures as your main log storage bucket.
  5. Set up Amazon CloudWatch Logs and AWS Config to monitor changes to your AWS resources and configurations. Route these logs to a separate S3 bucket or a dedicated monitoring solution, such as a SIEM platform.
  6. Use your centralized logging and monitoring solution (e.g., a SIEM platform) to analyze logs from various sources, including the syslogs, CloudTrail, and CloudWatch Logs. This will enable you to identify security incidents, vulnerabilities, or anomalies in your system.
  7. To update your DFD, include the additional components (e.g., Lambda functions, monitoring tools, and SIEM platform) as separate entities. Use arrows to represent the flow of data between these entities and the existing components. Label each arrow with a brief description of the data flow, such as “Merged logs” or “CloudTrail logs”.

Conclusion

Overall this was pretty impressive. ChatGPT hit on many of the main points I’d expect a early to mid-level security engineer to touch on. In a normal back and forth between a developer and a security engineer I’d expect to see more of a push from the security engineer to get the developer to define the system in more definite and descriptive terms. I think there’s definitely a value in developing a tuned threat modeling Chat AI that an engineer can bounce ideas/designs off of and get general best practice advice on what to think about from a security perspective.