Ethical_Hacker_and_Programmer_FALCON_with_LangChain.ipynb

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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/santosomar/ai-research/blob/main/Ethical_Hacker_and_Programmer_FALCON_with_LangChain.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DLibqkUaJ2p7",
        "outputId": "d38ca590-5441-4592-ee6a-a48e89b0ef00"
      },
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            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.4/7.4 MB\u001b[0m \u001b[31m15.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m42.2/42.2 kB\u001b[0m \u001b[31m4.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m244.2/244.2 kB\u001b[0m \u001b[31m24.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.4/1.4 MB\u001b[0m \u001b[31m18.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m92.6/92.6 MB\u001b[0m \u001b[31m9.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m268.8/268.8 kB\u001b[0m \u001b[31m20.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.8/7.8 MB\u001b[0m \u001b[31m54.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.3/1.3 MB\u001b[0m \u001b[31m31.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m90.0/90.0 kB\u001b[0m \u001b[31m7.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m49.4/49.4 kB\u001b[0m \u001b[31m3.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h"
          ]
        }
      ],
      "source": [
        "!pip install -q transformers einops accelerate langchain bitsandbytes xformers"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!nvidia-smi"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QDWlM5OwUIGv",
        "outputId": "94bc8123-e577-46a0-9b53-9d7a5dd4078c"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Sat Aug  5 18:01:39 2023       \n",
            "+-----------------------------------------------------------------------------+\n",
            "| NVIDIA-SMI 525.105.17   Driver Version: 525.105.17   CUDA Version: 12.0     |\n",
            "|-------------------------------+----------------------+----------------------+\n",
            "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
            "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n",
            "|                               |                      |               MIG M. |\n",
            "|===============================+======================+======================|\n",
            "|   0  Tesla T4            Off  | 00000000:00:04.0 Off |                    0 |\n",
            "| N/A   61C    P8    13W /  70W |      0MiB / 15360MiB |      0%      Default |\n",
            "|                               |                      |                  N/A |\n",
            "+-------------------------------+----------------------+----------------------+\n",
            "                                                                               \n",
            "+-----------------------------------------------------------------------------+\n",
            "| Processes:                                                                  |\n",
            "|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |\n",
            "|        ID   ID                                                   Usage      |\n",
            "|=============================================================================|\n",
            "|  No running processes found                                                 |\n",
            "+-----------------------------------------------------------------------------+\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from langchain import HuggingFacePipeline\n",
        "from transformers import AutoTokenizer, pipeline\n",
        "import torch\n",
        "\n",
        "model = \"tiiuae/falcon-7b-instruct\" # You can also use the larger model falcon-40b-instruct\n",
        "\n",
        "tokenizer = AutoTokenizer.from_pretrained(model)\n",
        "\n",
        "pipeline = pipeline(\n",
        "    \"text-generation\", #task\n",
        "    model=model,\n",
        "    tokenizer=tokenizer,\n",
        "    torch_dtype=torch.bfloat16,\n",
        "    trust_remote_code=True,\n",
        "    device_map=\"auto\",\n",
        "    max_length=10000,\n",
        "    do_sample=True,\n",
        "    top_k=10,\n",
        "    num_return_sequences=1,\n",
        "    eos_token_id=tokenizer.eos_token_id\n",
        ")\n"
      ],
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      "execution_count": 1,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)okenizer_config.json:   0%|          | 0.00/220 [00:00<?, ?B/s]"
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          "data": {
            "text/plain": [
              "Downloading (…)/main/tokenizer.json:   0%|          | 0.00/2.73M [00:00<?, ?B/s]"
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          "text": [
            "A new version of the following files was downloaded from https://huggingface.co/tiiuae/falcon-7b-instruct:\n",
            "- configuration_RW.py\n",
            ". Make sure to double-check they do not contain any added malicious code. To avoid downloading new versions of the code file, you can pin a revision.\n"
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            "A new version of the following files was downloaded from https://huggingface.co/tiiuae/falcon-7b-instruct:\n",
            "- modelling_RW.py\n",
            ". Make sure to double-check they do not contain any added malicious code. To avoid downloading new versions of the code file, you can pin a revision.\n"
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        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Xformers is not installed correctly. If you want to use memory_efficient_attention to accelerate training use the following command to install Xformers\n",
            "pip install xformers.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "llm = HuggingFacePipeline(pipeline = pipeline, model_kwargs = {'temperature':0})"
      ],
      "metadata": {
        "id": "N_0a78MA8GfD"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from langchain import PromptTemplate,  LLMChain\n",
        "\n",
        "template = \"\"\"\n",
        "You are an ethical hacker and programmer. Help the following question with brilliant answers.\n",
        "Question: {question}\n",
        "Answer:\"\"\"\n",
        "prompt = PromptTemplate(template=template, input_variables=[\"question\"])\n",
        "\n",
        "llm_chain = LLMChain(prompt=prompt, llm=llm)\n",
        "\n",
        "question = \"Create a python script to send a DNS packet using scapy with a secret payload \"\n",
        "\n",
        "print(llm_chain.run(question))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "O5aPbMqmKLZQ",
        "outputId": "f34cfa1e-fc0b-453e-dccf-70f392e021bc"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/generation/utils.py:1270: UserWarning: You have modified the pretrained model configuration to control generation. This is a deprecated strategy to control generation and will be removed soon, in a future version. Please use a generation configuration file (see https://huggingface.co/docs/transformers/main_classes/text_generation )\n",
            "  warnings.warn(\n",
            "Setting `pad_token_id` to `eos_token_id`:11 for open-end generation.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            " \n",
            "\n",
            "```python\n",
            "#!/usr/bin/env python3\n",
            "\n",
            "# Import the necessary libraries\n",
            "import scapy.all\n",
            "\n",
            "# Scapy commands to send DNS packet with payload\n",
            "packet = IP(dst=\"127.0.0.1\", flags=\"S\", timewais=0, seq=2, ack=10)\"\n",
            "packet.trunames = [\"example.com\".split()]\n",
            "packet.pkt.add(packet)\n",
            "\n",
            "# Scapy commands to inject a secret payload\n",
            "payload = b\"payload_to_send_over_dns\"\n",
            "payload_length = len(payload)\n",
            "\n",
            "# Scapy commands to create a DNS packet payload\n",
            "payload_dict = IP(dst=\"127.0.0.1\", flags=\"S\", seq=1, ack=1, payload=payload_length, payload=payload)\n",
            "IP(dst=[\"0.127.0.0\", \"8.8.4.4\"], payload=0xA, seq=2, ack=2)\n",
            "IP(dst=[\"8.8.4.4\", \"127.0.0.1\"], payload=payload_dict, seq=2, ack=2)\n",
            "```\n",
            "\n",
            "Explanation of commands used:\n",
            "- `IP(dst=\"127.0.0.1\", flags=\"S\", seq=2, ack=1)` creates a DNS request with the specified DNS server.\n",
            "- `IP(dst=[\"0.127.0.0\", \"8.8.4.4\"], payload=0xA, seq=2, ack=2)` creates a DNS request to the IP address specified.\n",
            "- `IP(dst=[\"8.8.4.4\", \"127.0.0.1\"], payload=payload_dict, seq=2, ack=2)` creates a second DNS request to the IP address specified to inject the payload.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "xVm7Qtlt-oL1"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}