07.1: Exercises: Code Summarization

In this week's exercises, your group will try out the various tasks for code summarization and reverse engineering using LLMs. Begin by completing the initial two parts of the codelab. Then, attempt the exercise your group has been assigned in the following Google Slide presentation:

Week 5 slides

Add screenshots that you can use to walkthrough how you performed the exercise. Your group will present your results for the exercise during the last hour of class. After completing the exercise you've been assigned, continue to the rest of the exercises in order to prepare for the week's homework assignment.

Using an LLM such as ChatGPT, Gemini , or Copilot to aid in summarizing code and reverse engineering its function can save a developer and an analyst a substantial amount of time and effort. However, to leverage this capability, one must be able to understand what tasks the models are reliably capable of doing to prevent errors. In this lab, you will utilize LLMs to analyze different code examples and determine whether the result is accurate. To begin with, change into the code directory for the exercises and install the packages.

cd cs475-src
git pull
cd 05*
virtualenv -p python3 env
source env/bin/activate
pip install -r requirements.txt

One of the benefits of using an LLM is its ability to use its broad knowledge base to explain code and commands that a particular user may not understand.

`iptables` commands

Consider the following set of commands for configuring rules using iptables, a network firewall tool for Linux.

iptables -A INPUT -i eth0 -p tcp -m multiport --dports 22,80,443 -m state --state NEW,ESTABLISHED -j ACCEPT
iptables -A OUTPUT -o eth0 -p tcp -m multiport --sports 22,80,443 -m state --state ESTABLISHED -j ACCEPT

Use LLMs to provide a concise summary of what the rules do showing any differences in output

Consider the following nginx configuration for a web server in https://mashimaro.cs.pdx.edu .

server {
      server_name mashimaro.cs.pdx.edu;
      root /var/www/html/mashimaro;
      index index.html;
      location / {
              try_files $uri $uri/ =404;
      }
      listen 443 ssl;
      ssl_certificate /etc/letsencrypt/live/mashimaro/fullchain.pem;
      ssl_certificate_key /etc/letsencrypt/live/mashimaro/privkey.pem;
      include /etc/letsencrypt/options-ssl-nginx.conf;
      ssl_dhparam /etc/letsencrypt/ssl-dhparams.pem;
}

server {
      if ($host = mashimaro.cs.pdx.edu) {
          return 301 https://$host$request_uri;
      }
      server_name mashimaro.cs.pdx.edu;
      listen 80;
      return 404;
}

Use LLMs to provide a concise summary of what the rules do showing any differences in output

Terraform and other infrastructure-as-code solutions provide a way of declaratively defining infrastructure that can then be deployed in a reliable, reproducible manner. Consider the Terraform specification file below that deploys a single virtual machine on Google Cloud Platform.

provider "google" {
  credentials = file("tf-lab.json")
  project     = "YOUR_PROJECT_ID"
  region      = "us-west1"
}

resource "google_compute_address" "static" {
  name = "ipv4-address"
}

resource "google_compute_instance" "default" {
  name         = "tf-lab-vm"
  machine_type = "e2-medium"
  zone         = "us-west1-b"

  boot_disk {
    initialize_params {
      image = "ubuntu-os-cloud/ubuntu-2204-jammy-v20240501"
    }
  }

  network_interface {
    network = "default"
    access_config {
      nat_ip = google_compute_address.static.address
    }
  }
}

output "ip" {
  value = google_compute_instance.default.network_interface.0.access_config.0.nat_ip
}

Use LLMs to provide an explanation for each line in the configuration showing any differences in output

Docker containers, which can be seen as virtual operating systems, are often used to deploy services in cloud environments. Containers are instantiated from images that are specified and built from a Dockerfile configuration. For beginners, parsing a configuration can be difficult. An LLM can potentially aid in understanding such files. Below is a Dockerfile for a multi-stage container build.

FROM python:3.5.9-alpine3.11 as builder

COPY . /app

WORKDIR /app

RUN pip install --no-cache-dir -r requirements.txt && pip uninstall -y pip && rm -rf /usr/local/lib/python3.5/site-packages/*.dist-info README

FROM python:3.5.9-alpine3.11

COPY --from=builder /app /app

COPY --from=builder /usr/local/lib/python3.5/site-packages/ /usr/local/lib/python3.5/site-packages/

WORKDIR /app

ENTRYPOINT ["python3","app.py"]

Use LLMs to provide an explanation for each line in the configuration showing any differences in output

Kubernetes is a system for declaratively specifying infrastructure, deploying it, and maintaining its operation, often using containers and container images. Below is a simple configuration for a web application.

apiVersion: v1
kind: ReplicationController
metadata:
  name: guestbook-replicas
spec:
  replicas: 3
  template:
    metadata:
      labels:
        app: guestbook
        tier: frontend
    spec:
      containers:
      - name: guestbook-app
        image: gcr.io/YOUR_PROJECT_ID/gcp_gb
        env:
        - name: PROCESSES
          value: guestbook
        - name: PORT
          value: "8000"
        ports:
        - containerPort: 8000
---
apiVersion: v1
kind: Service
metadata:
  name: guestbook-lb
  labels:
    app: guestbook
    tier: frontend
spec:
  type: LoadBalancer
  ports:
  - port: 80
    targetPort: 8000
  selector:
    app: guestbook
    tier: frontend

Use LLMs to provide an explanation for each line in the configuration showing any differences in output

iptables commands

`iptables` commands