├── .gitignore ├── Dockerfile ├── LICENSE ├── Makefile ├── README.md ├── app ├── __init__.py ├── main.py ├── misc.py └── summarizer.py ├── docker-compose.yml ├── env.example ├── requirements.txt └── tests ├── apt29-oneline.txt └── apt29.txt /.gitignore: -------------------------------------------------------------------------------- 1 | # Byte-compiled / optimized / DLL files 2 | __pycache__/ 3 | *.py[cod] 4 | *$py.class 5 | 6 | # C extensions 7 | *.so 8 | 9 | # Distribution / packaging 10 | .Python 11 | build/ 12 | develop-eggs/ 13 | dist/ 14 | downloads/ 15 | eggs/ 16 | .eggs/ 17 | lib/ 18 | lib64/ 19 | parts/ 20 | sdist/ 21 | var/ 22 | wheels/ 23 | share/python-wheels/ 24 | *.egg-info/ 25 | .installed.cfg 26 | *.egg 27 | MANIFEST 28 | 29 | # PyInstaller 30 | # Usually these files are written by a python script from a template 31 | # before PyInstaller builds the exe, so as to inject date/other infos into it. 32 | *.manifest 33 | *.spec 34 | 35 | # Installer logs 36 | pip-log.txt 37 | pip-delete-this-directory.txt 38 | 39 | # Unit test / coverage reports 40 | htmlcov/ 41 | .tox/ 42 | .nox/ 43 | .coverage 44 | .coverage.* 45 | .cache 46 | nosetests.xml 47 | coverage.xml 48 | *.cover 49 | *.py,cover 50 | .hypothesis/ 51 | .pytest_cache/ 52 | cover/ 53 | 54 | # Translations 55 | *.mo 56 | *.pot 57 | 58 | # Django stuff: 59 | *.log 60 | local_settings.py 61 | db.sqlite3 62 | db.sqlite3-journal 63 | 64 | # Flask stuff: 65 | instance/ 66 | .webassets-cache 67 | 68 | # Scrapy stuff: 69 | .scrapy 70 | 71 | # Sphinx documentation 72 | docs/_build/ 73 | 74 | # PyBuilder 75 | .pybuilder/ 76 | target/ 77 | 78 | # Jupyter Notebook 79 | .ipynb_checkpoints 80 | 81 | # IPython 82 | profile_default/ 83 | ipython_config.py 84 | 85 | # pyenv 86 | # For a library or package, you might want to ignore these files since the code is 87 | # intended to run in multiple environments; otherwise, check them in: 88 | # .python-version 89 | 90 | # pipenv 91 | # According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. 92 | # However, in case of collaboration, if having platform-specific dependencies or dependencies 93 | # having no cross-platform support, pipenv may install dependencies that don't work, or not 94 | # install all needed dependencies. 95 | #Pipfile.lock 96 | 97 | # poetry 98 | # Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control. 99 | # This is especially recommended for binary packages to ensure reproducibility, and is more 100 | # commonly ignored for libraries. 101 | # https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control 102 | #poetry.lock 103 | 104 | # pdm 105 | # Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control. 106 | #pdm.lock 107 | # pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it 108 | # in version control. 109 | # https://pdm.fming.dev/#use-with-ide 110 | .pdm.toml 111 | 112 | # PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm 113 | __pypackages__/ 114 | 115 | # Celery stuff 116 | celerybeat-schedule 117 | celerybeat.pid 118 | 119 | # SageMath parsed files 120 | *.sage.py 121 | 122 | # Environments 123 | .env 124 | .venv 125 | env/ 126 | venv/ 127 | ENV/ 128 | env.bak/ 129 | venv.bak/ 130 | 131 | # Spyder project settings 132 | .spyderproject 133 | .spyproject 134 | 135 | # Rope project settings 136 | .ropeproject 137 | 138 | # mkdocs documentation 139 | /site 140 | 141 | # mypy 142 | .mypy_cache/ 143 | .dmypy.json 144 | dmypy.json 145 | 146 | # Pyre type checker 147 | .pyre/ 148 | 149 | # pytype static type analyzer 150 | .pytype/ 151 | 152 | # Cython debug symbols 153 | cython_debug/ 154 | 155 | # PyCharm 156 | # JetBrains specific template is maintained in a separate JetBrains.gitignore that can 157 | # be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore 158 | # and can be added to the global gitignore or merged into this file. For a more nuclear 159 | # option (not recommended) you can uncomment the following to ignore the entire idea folder. 160 | #.idea/ 161 | -------------------------------------------------------------------------------- /Dockerfile: -------------------------------------------------------------------------------- 1 | #FROM python:3.11-bookworm 2 | # FROM python:alpine 3 | FROM python:3.11-slim-bullseye 4 | 5 | # create a working directory 6 | RUN mkdir /app 7 | WORKDIR /app 8 | 9 | # copy the requirements.txt file 10 | COPY requirements.txt . 11 | 12 | # install the dependencies 13 | RUN pip install -r requirements.txt 14 | 15 | # Install needed debian packages 16 | #RUN apt update && apt install -y build-essential gcc 17 | 18 | # copy the main files 19 | COPY . . 20 | # COPY .env . 21 | 22 | 23 | # expose the port for the FastAPI application 24 | EXPOSE 9090 25 | 26 | # run the FastAPI application 27 | CMD ["uvicorn", "main:app", "--reload", "--host", "0.0.0.0", "--port", "9090", "--reload"] 28 | 29 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | GNU AFFERO GENERAL PUBLIC LICENSE 2 | Version 3, 19 November 2007 3 | 4 | Copyright (C) 2007 Free Software Foundation, Inc. 5 | Everyone is permitted to copy and distribute verbatim copies 6 | of this license document, but changing it is not allowed. 7 | 8 | Preamble 9 | 10 | The GNU Affero General Public License is a free, copyleft license for 11 | software and other kinds of works, specifically designed to ensure 12 | cooperation with the community in the case of network server software. 13 | 14 | The licenses for most software and other practical works are designed 15 | to take away your freedom to share and change the works. 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It is safest 628 | to attach them to the start of each source file to most effectively 629 | state the exclusion of warranty; and each file should have at least 630 | the "copyright" line and a pointer to where the full notice is found. 631 | 632 | 633 | Copyright (C) 634 | 635 | This program is free software: you can redistribute it and/or modify 636 | it under the terms of the GNU Affero General Public License as published 637 | by the Free Software Foundation, either version 3 of the License, or 638 | (at your option) any later version. 639 | 640 | This program is distributed in the hope that it will be useful, 641 | but WITHOUT ANY WARRANTY; without even the implied warranty of 642 | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 643 | GNU Affero General Public License for more details. 644 | 645 | You should have received a copy of the GNU Affero General Public License 646 | along with this program. If not, see . 647 | 648 | Also add information on how to contact you by electronic and paper mail. 649 | 650 | If your software can interact with users remotely through a computer 651 | network, you should also make sure that it provides a way for users to 652 | get its source. For example, if your program is a web application, its 653 | interface could display a "Source" link that leads users to an archive 654 | of the code. There are many ways you could offer source, and different 655 | solutions will be better for different programs; see section 13 for the 656 | specific requirements. 657 | 658 | You should also get your employer (if you work as a programmer) or school, 659 | if any, to sign a "copyright disclaimer" for the program, if necessary. 660 | For more information on this, and how to apply and follow the GNU AGPL, see 661 | . 662 | -------------------------------------------------------------------------------- /Makefile: -------------------------------------------------------------------------------- 1 | all: 2 | docker build -t stochastic_alex:0.1 . --network=host && docker compose down && docker compose --env-file .env up -d 3 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Stochastic CTI Extractor 2 | Interface LLMs from within MISP to extract TTPs and threat intel from CTI reports 3 | 4 | 5 | 6 | This RESTful API service accepts markdown text as input. It expects that the text was a CTI report (some blog post or so ) and cleaned (i.e. no links, advertisement etc in it). 7 | It will then ask an LLM to summarize the CTI report and extract relevant information on threat actors, TTPs, etc. out. 8 | Finally, it will send the results back to the called (MISP in this case). 9 | 10 | 11 | ## workflow and architecture sketch 12 | 13 | ![image](https://github.com/aaronkaplan/stochastic_alex/assets/750019/104f793a-80b2-45cd-9fae-594d58212f36) 14 | 15 | -------------------------------------------------------------------------------- /app/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/aaronkaplan/stochasticCTIExtractor/93c900b34ef2e6d4be8aa1f5715296e68f959eb4/app/__init__.py -------------------------------------------------------------------------------- /app/main.py: -------------------------------------------------------------------------------- 1 | from fastapi import FastAPI, HTTPException, Header, Depends 2 | from fastapi.responses import JSONResponse 3 | from pydantic import BaseModel 4 | 5 | from summarizer import Summarizer 6 | 7 | 8 | app = FastAPI(title="Stochastic Alex") 9 | 10 | 11 | class TextData(BaseModel): 12 | text: str 13 | 14 | 15 | # Define your list of valid API keys 16 | valid_api_keys = ["1234test", "5678test"] 17 | 18 | 19 | # Dependency to validate the API key 20 | def get_valid_api_key(x_api_key: str = Header(None)) -> str: 21 | if x_api_key not in valid_api_keys: 22 | raise HTTPException(status_code=401, detail="Unauthorized") 23 | return x_api_key 24 | 25 | 26 | @app.post("/summarize", tags=["Manual summarization of a single article"]) 27 | async def summarize_text(data: TextData, api_key: str = Depends(get_valid_api_key)): 28 | """Summarize an article given by {url} . Returns a JSON answer.""" 29 | print(f"About to summarize '{data.text[30:]}'") 30 | text = data.text 31 | 32 | summarizer = Summarizer() 33 | summary = summarizer.summarize_via_openai(text) 34 | # return JSONResponse(content=summary, status_code=200) 35 | return summary 36 | 37 | -------------------------------------------------------------------------------- /app/misc.py: -------------------------------------------------------------------------------- 1 | 2 | import uuid 3 | import os 4 | 5 | def save_string_to_custom_temp_file(input_string: str) -> str: 6 | # Generate a unique file name using UUID 7 | unique_filename = str(uuid.uuid4()) + ".txt" 8 | 9 | # Define a temporary directory (you can customize this path) 10 | temp_dir = "/tmp" 11 | 12 | # Full path to the temporary file 13 | temp_file_path = os.path.join(temp_dir, unique_filename) 14 | 15 | # Write the string to the temporary file 16 | with open(temp_file_path, "w") as f: 17 | f.write(input_string) 18 | 19 | return temp_file_path -------------------------------------------------------------------------------- /app/summarizer.py: -------------------------------------------------------------------------------- 1 | """Langchain based summarizer""" 2 | 3 | import os 4 | # import pandas as pd 5 | from typing import Union, Dict 6 | from distutils.util import strtobool 7 | import tempfile 8 | 9 | import openai 10 | from langchain.chat_models import ChatOpenAI 11 | from langchain.llms import AzureOpenAI 12 | from langchain.llms import TextGen 13 | from langchain.chains import ConversationChain 14 | from langchain.memory import ConversationBufferMemory 15 | from langchain.prompts import ChatPromptTemplate 16 | from langchain.prompts import PromptTemplate 17 | from langchain.output_parsers import ResponseSchema 18 | from langchain.output_parsers import StructuredOutputParser 19 | from langchain.chains.summarize import load_summarize_chain 20 | from langchain.text_splitter import TokenTextSplitter 21 | from langchain.document_loaders.text import TextLoader 22 | from langchain.prompts import PromptTemplate 23 | 24 | from misc import save_string_to_custom_temp_file 25 | 26 | 27 | debug = True 28 | 29 | from dotenv import load_dotenv, find_dotenv 30 | _ = load_dotenv(find_dotenv()) # read local .env file 31 | 32 | DRY_RUN = strtobool(os.environ['DRY_RUN']) 33 | MAXLEN=16000 34 | PROMPTLEN=500 35 | SUMMARYLEN=1000 36 | USE_AZURE = strtobool(os.environ['USE_AZURE']) 37 | if USE_AZURE: 38 | ENGINE=os.environ['ENGINE'] 39 | MODEL="gpt4-32k" 40 | MAXLEN=32000 41 | else: 42 | ENGINE=None 43 | MODEL="gpt-3.5-turbo" 44 | # MODEL="llama2" 45 | 46 | 47 | openai.api_key = os.environ['OPENAI_API_KEY'] 48 | 49 | 50 | # ################################ 51 | # templates. FIXME these should move to a template DB 52 | CTI_TEMPLATE_INFO_EXTRACTION = """\ 53 | For the following text, extract the following information: \ 54 | ThreatActor: who is the threat actor? Format as JSON string.\ 55 | TTPs: what tools, techniques and procedures (TTPs) are they using? Format as JSON list of strings.\ 56 | Targets: whom are they targetting? Format as JSON list of strings.\ 57 | IoCs: all DNS domain names, hashes, path names, process names, registry keys. Format as JSON list of strings. 58 | Goals: what are the goals of the Threat Actor? Format as string. 59 | 60 | Format the output as JSON with the following keys: 61 | ThreatActor 62 | Goals 63 | TTPs 64 | Targets 65 | IoCs 66 | 67 | text: {text} 68 | """ 69 | 70 | 71 | # CouldWeBeAffected: Are we (the EU Institions, Bodies and Agencies) affected by the threat? Is the Threat Actor targetting the EU Institions, Bodies and Agencies? Answer True if yes, False if not. Answer None if not known. 72 | # ThreatActor: who is the threat actor? If multiple, make a JSON list of strings. Format as a JSON list of strings. Answer ['unknown'] if not known. 73 | 74 | CTI_TEMPLATE_SUMMARIZATION = """\ 75 | 76 | ThreatActor: Who is the threat actor? If multiple, make a comma separated list. Format as a JSON string. Answer 'unknown' if not known. 77 | AttributedCountry: which country is suppossedly behind this Threat Actor? Format as a single JSON string. Answer 'unknown' if not known. 78 | Type: How would you classify the Threat or Threat Actor? Possible answers: 'Crimeware', 'Nation-state', 'Developments in IT Security', 'Hacktivism', 'Information Warfare', 'unknown'. If multiple, separate by comma. Format as single JSON string. Answer 'unknown' if not known." 79 | Motivation: what is the motivation, what are the goals of the Threat Actor? One of 'Espionage', 'Sabotage', 'Financial', 'Propaganda', 'Other', 'unknown' . Format as a single JSON string. One sentence only. Answer 'unknown' if unknown. 80 | ExecutiveSummary: a short 200 word summary of the contents of the report. Focus on the specifics of this report. Leave out the general descriptions. Format a a single JSON string. 81 | CouldWeBeAffected: Is the Threat Actor targetting Europe or EU Institutions, Bodies or Agencies? Answer True if yes, False if not. Answer None if not known. 82 | 83 | Format the output as JSON with the following keys: 84 | AI_ThreatActor 85 | AI_AttributedCountry 86 | AI_Type 87 | AI_Motivation 88 | AI_ExecutiveSummary 89 | AI_CouldWeBeAffected 90 | 91 | text: {text} 92 | 93 | """ 94 | # {format_instructions} 95 | 96 | THREAT_ACTOR_SCHEMA="Who is the threat actor? If multiple, make a comma separated list. Format as a JSON string. Answer 'unknown' if not known." 97 | ATTRIBUTEDCOUNTRY_SCHEMA="Which country is suppossedly behind this Threat Actor? Format as a single JSON string. Answer 'unknown' if not known." 98 | TYPE_SCHEMA="How would you classify the Threat or Threat Actor? Possible answers: 'Crimeware', 'Nation-state', 'Developments in IT Security', 'Hacktivism', 'Information Warfare', 'unknown'. If multiple, separate by comma. Format as single JSON string. Answer 'unknown' if not known." 99 | # MOTIVATION_SCHEMA="What is the motivation, what are the goals of the Threat Actor? Format as a single JSON string. One sentence only. Answer 'unknown' if unknown." 100 | MOTIVATION_SCHEMA="What is the motivation, what are the goals of the Threat Actor? One of 'Espionage', 'Sabotage', 'Financial', 'Propaganda', 'Other', 'unknown' . Format as a single JSON string. One sentence only. Answer 'unknown' if unknown." 101 | EXECUTIVESUMMARY_SCHEMA= "A short 200 word summary of the contents of the report. Focus on the specifics of this report. Leave out the general descriptions. Format a a single JSON string." 102 | #COULD_WE_BE_AFFECTED_SCHEMA="Are we (the EU Institions, Bodies and Agencies) affected by the threat? Is the Threat Actor targetting the EU Institions, Bodies and Agencies? Answer True if yes, False if not. Answer None if not known." 103 | COULD_WE_BE_AFFECTED_SCHEMA="Is the Threat Actor targetting Europe or EU Institutions, Bodies or Agencies? Answer True if yes, False if not. Answer None if not known." 104 | # COULD_WE_BE_AFFECTED_SCHEMA="Does the text say that the Threat Actor is targetting Europe or EU Institutions? Answer True if yes, False if not. Answer None if not known." 105 | 106 | ta_schema = ResponseSchema(name="AI_ThreatActor", description=THREAT_ACTOR_SCHEMA, type="string") 107 | country_schema = ResponseSchema(name="AI_AttributedCountry", description=ATTRIBUTEDCOUNTRY_SCHEMA, type="string") 108 | type_schema = ResponseSchema(name="AI_Type", description=TYPE_SCHEMA, type="string") 109 | motivation_schema = ResponseSchema(name="AI_Motivation", description=MOTIVATION_SCHEMA, type="string") 110 | executivesummary_schema = ResponseSchema(name="AI_ExecutiveSummary", description=EXECUTIVESUMMARY_SCHEMA, type="string") 111 | could_we_be_affected = ResponseSchema(name="AI_CouldWeBeAffected", description=COULD_WE_BE_AFFECTED_SCHEMA, type="Union[boolean, None]") 112 | 113 | response_schemas = [ta_schema, country_schema, type_schema, motivation_schema, executivesummary_schema, could_we_be_affected] 114 | 115 | 116 | class Summarizer(): 117 | """A summarizer class, using langchain and recursive summarization tricks.""" 118 | 119 | def __init__(self): 120 | self.model = MODEL 121 | if USE_AZURE: 122 | self.llm = AzureOpenAI ( deployment_name=os.environ['ENGINE'], temperature=0.0) 123 | else: 124 | self.llm = ChatOpenAI(model=self.model, temperature=0.0) 125 | # self.llm = TextGen(model_url=os.environ['OPENAI_API_BASE']) 126 | self.memory = ConversationBufferMemory() 127 | self.conversation = ConversationChain( 128 | llm=self.llm, 129 | memory = self.memory, 130 | verbose=False 131 | ) 132 | 133 | self.prompt_template_summarization = PromptTemplate.from_template(CTI_TEMPLATE_SUMMARIZATION) 134 | if debug: 135 | print(self.prompt_template_summarization) 136 | 137 | 138 | def summarize_via_openai(self, text: str) -> Dict: # Union[None, str]: 139 | """ 140 | Summarize the content of a given text using OpenAI's with chunking. 141 | 142 | Parameters: 143 | - text (str): A string containing the CTI report 144 | 145 | Returns: 146 | - str or None: Returns the summary of the content of the URL if successful. Returns None if the URL is invalid or if any exception occurs. 147 | 148 | Raises: 149 | - Exception: An exception is raised if there is any issue in loading the URL or during the summarization process. The exception's message is printed to the console. 150 | 151 | Notes: 152 | - If the variable DRY_RUN is set in the environment, nothing will be sent to the llm 153 | - It utilizes a token text splitter (`TokenTextSplitter`) with specific chunk sizes and encoding for GPT-3 to split the content into manageable pieces. 154 | - Summarization is done using a "map-reduce" chain for processing the chunks of text. The prompt templates for summarization are defined by `PromptTemplate`. 155 | 156 | Example: 157 | >>> summarize_via_openai(self, "my long CTI report") 158 | "This is the summarized content." 159 | 160 | """ 161 | text_splitter = TokenTextSplitter(encoding_name="gpt3", 162 | # model_name=self.model, 163 | model_name="gpt-3.5-turbo", 164 | chunk_size=MAXLEN-PROMPTLEN-SUMMARYLEN) 165 | 166 | if not text: 167 | print(f"Warning: empty text submitted. Returning what must be returned") 168 | return None 169 | 170 | # save the text temporarily XXX FIXME, actually not needed XXX 171 | temp_file_name = save_string_to_custom_temp_file(text) 172 | print(f"Wrote contents to {temp_file_name}") 173 | 174 | try: 175 | loader = TextLoader(temp_file_name, autodetect_encoding=True) 176 | 177 | # chunk it up, baby 178 | try: 179 | docs = loader.load_and_split(text_splitter) 180 | if debug: 181 | print(type(docs)) 182 | print(f"{len(docs)=}") 183 | print(docs) 184 | except Exception as ex: 185 | print(f"Error occured when loading via loader: {str(ex)}") 186 | return {"error": ex} 187 | 188 | if DRY_RUN: 189 | return {"answer": "in dry-run mode, not summarizing"} 190 | 191 | output_parser = StructuredOutputParser.from_response_schemas(response_schemas) 192 | format_instructions = output_parser.get_format_instructions() 193 | if debug: 194 | print(f'FORMAT INSTRUCTIONS: {format_instructions}') 195 | 196 | # now summarize all the chunks with map reduce 197 | # prompt = PromptTemplate(template=CTI_TEMPLATE_SUMMARIZATION, input_variables=["text", "format_instructions"]) 198 | prompt = PromptTemplate.from_template(template=CTI_TEMPLATE_SUMMARIZATION) 199 | chain = load_summarize_chain(self.llm, chain_type="map_reduce", 200 | return_intermediate_steps=False, 201 | map_prompt=prompt, 202 | combine_prompt=prompt, 203 | verbose=False) 204 | response = chain(docs, return_only_outputs=True) 205 | if debug: 206 | print(80*"=") 207 | print(f"Type(response): {type(response)}; Response: {response}") 208 | print(80*"=") 209 | 210 | return dict(output_parser.parse(response['output_text'])) 211 | except Exception as ex: 212 | print(f"An error occurred with input '{text[30:]}'. Error: {str(ex)}") 213 | return {"error": str(ex)} 214 | -------------------------------------------------------------------------------- /docker-compose.yml: -------------------------------------------------------------------------------- 1 | version: "3" 2 | services: 3 | # traefik reverse proxy 4 | traefik: 5 | image: traefik:v2.4 6 | command: 7 | - "--api.insecure=true" 8 | - "--providers.docker=true" 9 | - "--providers.docker.exposedbydefault=false" 10 | - "--entrypoints.web.address=:80" 11 | - "--entrypoints.websecure.address=:443" 12 | - "--certificatesresolvers.myresolver.acme.httpchallenge=true" 13 | - "--certificatesresolvers.myresolver.acme.httpchallenge.entrypoint=web" 14 | - "--certificatesresolvers.myresolver.acme.email=aaron+letsencrypt@lo-res.org" 15 | - "--certificatesresolvers.myresolver.acme.storage=/letsencrypt/acme.json" 16 | ports: 17 | - "80:80" 18 | - "443:443" 19 | volumes: 20 | - "/var/run/docker.sock:/var/run/docker.sock:ro" 21 | - "./letsencrypt:/letsencrypt" 22 | networks: 23 | - web 24 | labels: 25 | - "traefik.http.middlewares.redirect-https.redirectscheme.scheme=https" 26 | - "traefik.http.routers.global.rule=HostRegexp(`{host:.+}`)" 27 | - "traefik.http.routers.global.middlewares=redirect-https" 28 | - "traefik.http.routers.global.entrypoints=web" 29 | 30 | # The main microservice, serving the OpenAI based meeting minutes xscriber 31 | # 32 | stochastic_alex: 33 | image: stochastic_alex:0.1 34 | build: 35 | context: . 36 | dockerfile: Dockerfile 37 | environment: 38 | PYTHON_PATH: /app 39 | env_file: .env 40 | ports: 41 | - "9090:9090" 42 | dns: 8.8.8.8 43 | labels: 44 | - "traefik.enable=true" 45 | - "traefik.http.routers.xscriber.rule=Host(`misp-llm.lo-res.org`)" 46 | - "traefik.http.routers.xscriber.entrypoints=websecure" 47 | - "traefik.http.routers.xscriber.tls.certresolver=myresolver" 48 | - "traefik.http.services.xscriber.loadbalancer.server.port=9090" 49 | volumes: 50 | - "./app:/app" # map /uploads from host to /tmp in the container 51 | networks: 52 | - web 53 | 54 | networks: 55 | web: 56 | external: true 57 | -------------------------------------------------------------------------------- /env.example: -------------------------------------------------------------------------------- 1 | # Direct OPENAI API settings: 2 | OPENAI_API_KEY=sk-8L... 3 | OPENAI_MODEL="gpt-4-32k" 4 | 5 | -------------------------------------------------------------------------------- /requirements.txt: -------------------------------------------------------------------------------- 1 | fastapi 2 | uvicorn 3 | openai 4 | #langchain[llms] 5 | langchain 6 | python-dotenv 7 | tiktoken 8 | pytest 9 | python-magic 10 | selenium 11 | beautifulsoup4 12 | requests-mock 13 | -------------------------------------------------------------------------------- /tests/apt29-oneline.txt: -------------------------------------------------------------------------------- 1 | { "text": "Russian APT29 hackers' stealthy malware undetected for years By Ionut Ilascu January 27, 2022 09:23 AM 0 Cozy Bear Russian hackers camouflage new malware as legitimate files EXCLUSIVE: Hackers associated with the Russian Federation Foreign Intelligence Service (SVR) continued their incursions on networks of multiple organizations after the SolarWinds supply-chain compromise using two recently discovered sophisticated threats. The malicious implants are a variant of the GoldMax backdoor for Linux systems and a completely new malware family that cybersecurity company CrowdStrike now tracks as TrailBlazer. Both threats have been used in StellarParticle campaigns since at least mid-2019 but were identified only two years later, during incident response investigations. StellarParticle attacks have been attributed to the APT29 hacking group has been running cyber espionage campaigns for more than 12 years and is also known as CozyBear, The Dukes, and Yttrium. Stealing cookies for MFA bypass In a report shared exclusively with BleepingComputer, cybersecurity company CrowdStrike today describes in detail the latest tactics, techniques, and procedures (TTPs) observed in cyberattacks from the Cozy Bear state-sponsored hackers. While some of the techniques are somewhat common today, Cozy Bear has been using them long before they became popular: credential hopping hijacking Office 365 (O365) Service Principal and Application bypassing multi-factor authentication (MFA) by stealing browser cookies stealing credentials using Get-ADReplAccount Credential hopping was the first stage of the attack, allowing the threat actor to log into Office 365 from an internal server that the hackers reached through a compromised public-facing system. source: CrowdStrike CrowdStrike says that this technique is hard to spot in environments with little visibility into identity usage since hackers could use more than one domain administrator account." } 2 | -------------------------------------------------------------------------------- /tests/apt29.txt: -------------------------------------------------------------------------------- 1 | Russian APT29 hackers' stealthy malware undetected for years 2 | By Ionut Ilascu 3 | January 27, 2022 09:23 AM 0 4 | Cozy Bear Russian hackers camouflage new malware as legitimate files 5 | 6 | EXCLUSIVE: Hackers associated with the Russian Federation Foreign Intelligence Service (SVR) continued their incursions on networks of multiple organizations after the SolarWinds supply-chain compromise using two recently discovered sophisticated threats. 7 | 8 | The malicious implants are a variant of the GoldMax backdoor for Linux systems and a completely new malware family that cybersecurity company CrowdStrike now tracks as TrailBlazer. 9 | 10 | Both threats have been used in StellarParticle campaigns since at least mid-2019 but were identified only two years later, during incident response investigations. 11 | 12 | StellarParticle attacks have been attributed to the APT29 hacking group has been running cyber espionage campaigns for more than 12 years and is also known as CozyBear, The Dukes, and Yttrium. 13 | 14 | Stealing cookies for MFA bypass 15 | In a report shared exclusively with BleepingComputer, cybersecurity company CrowdStrike today describes in detail the latest tactics, techniques, and procedures (TTPs) observed in cyberattacks from the Cozy Bear state-sponsored hackers. 16 | 17 | While some of the techniques are somewhat common today, Cozy Bear has been using them long before they became popular: 18 | 19 | credential hopping 20 | hijacking Office 365 (O365) Service Principal and Application 21 | bypassing multi-factor authentication (MFA) by stealing browser cookies 22 | stealing credentials using Get-ADReplAccount 23 | Credential hopping was the first stage of the attack, allowing the threat actor to log into Office 365 from an internal server that the hackers reached through a compromised public-facing system. 24 | 25 | 26 | source: CrowdStrike 27 | CrowdStrike says that this technique is hard to spot in environments with little visibility into identity usage since hackers could use more than one domain administrator account. 28 | 29 | Bypassing MFA to access cloud resources by stealing browser cookies has been used since before 2020. CrowdStrike says that APT29 kept a low profile after decrypting the authentication cookies, likely offline, by using the Cookie Editor extension for Chrome to replay them; they deleted the extension afterward. 30 | 31 | This extension permitted bypassing MFA requirements, as the cookies, replayed through the Cookie Editor extension, allowed the threat actor to hijack the already MFA-approved session of a targeted user - CrowdStrike 32 | 33 | This allowed them to move laterally on the network and reach the next stage of the attack, connecting to the victim's O365 tenant for the next stage of the attack. 34 | 35 | CrowdStrike's report describes the steps that APT29 took to achieve persistence in a position that allowed them to read any email and SharePoint or OneDrive files of the compromised organization. 36 | 37 | GoldMax for Linux and TrailBlazer 38 | During their incident response work on APT29 StellarParticle attacks, CrowdStrike's researchers used the User Access Logging (UAL) database to identify earlier malicious account usage, which led to finding the GoldMax for Linux and TrailBlazer malware. 39 | 40 | CrowdStrike says that TrailBlazer is a completely new malware family, while GoldMax for Linux backdoor 'is almost identical in functionality and implementation to the previously identified May 2020 Windows variant.' 41 | 42 | The researchers believe that the little differences are between the two GoldMax versions are due to the continuous improvements from the developer for long-term detection evasion. 43 | 44 | GoldMax was likely used for persistence (a crontab with a '@reboot' line for a non-root user) over long periods in StellarParticle campaigns. The backdoor stayed undetected by posing as a legitimate file in a hidden directory. 45 | 46 | The TrailBlazer implant also hid under the name of a legitimate file and it was configured for persistence using the Windows Management Instrumentation (WMI) Event Subscriptions, a relatively new technique in 2019, the earliest known date for its deployment on victim systems. 47 | 48 | TrailBlazer managed to keep communication with the command and control (C2) server covert by masking it as legitimate Google Notifications HTTP requests. 49 | 50 | CrowdStrike notes that the implant has modular functionality and 'a very low prevalence' and that it shares similarities with other malware families used by the same threat actor, such as GoldMax and Sunburst (both used in the SolarWinds supply-chain attack). 51 | 52 | Tim Parisi, Director of Professional Services at CrowdStrike, told BleepingComputer that the covert activity of the two malware pieces delayed the discovery of the two malware pieces, as the researchers found them in mid-2021. 53 | 54 | Recon and move to Office 365 55 | After gaining access to a target organization's infrastructure and established persistence, APT29 hackers took every opportunity to collect intelligence that would allow them to further the attack. 56 | 57 | One constant tactic was to draw information from the victim's internal knowledge repositories, the so-called wikis. These documents can hold sensitive details specific to various services and products in the organization. 58 | 59 | 'This information included items such as product/service architecture and design documents, vulnerabilities and step-by-step instructions to perform various tasks. Additionally, the threat actor viewed pages related to internal business operations such as development schedules and points of contact. In some instances these points of contact were subsequently targeted for further data collection' - CrowdStrike 60 | 61 | Parisi told us that accessing company wikis was a common APT29 reconnaissance activity in the investigated StellarParticle attacks. 62 | 63 | CrowdStrike's deep dive into APT29's StellarParticle campaigns offers details on how the threat actor connected to the victim's O365 tenant through the Windows Azure Active Directory PowerShell Module, and performed enumeration queries for roles, members, users, domains, accounts, or a service principal's credentials. 64 | 65 | When analyzing the log entries, the researchers noticed that the threat actor also executed the AddServicePrincipalCredentials command. 66 | 67 | 'CrowdStrike analyzed the configuration settings in the victim's O365 tenant and discovered that a new secret had been added to a built-in Microsoft Azure AD Enterprise Application, Microsoft StaffHub Service Principal, which had Application level permissions” - CrowdSrike 68 | 69 | The adversary had added a new secret to the application and set its validity for more than 10 years, the researchers note. 70 | 71 | The permission level obtained this way let hackers access all mail and SharePoint/OneDrive files in the company and allowed them to 'create new accounts and assign administrator privileges to any account in the organization.' 72 | 73 | Maintaining persistence 74 | Once Cozy Bear/APT29 established persistence in a target organization they would maintain it for as long as possible, sometimes helped by the poor security hygiene of the compromised organization. 75 | 76 | The longest time the threat actor spent inside an organization was two years, Parisi told BleepingComputer. Persisting this long would not be possible without some effort from the hackers, since organizations often rotate credentials as a security precaution. 77 | 78 | To prevent losing access, Cozy Bear hackers would periodically refresh the stolen credentials by stealing new ones, oftentimes via Mimikatz. 79 | 80 | In at least one case, though, the administrators of the compromised company reset their passwords to the same ones, thus defeating the purpose of credential rotation. 81 | 82 | Cozy Bear hackers are some of the most sophisticated threat actors in the cyber espionage world, with top skills to infiltrate and stay undetected on a company's infrastructure for long periods. 83 | 84 | During the StellarParticle attacks, they demonstrated expert knowledge in Azure, Office 365, and Active Directory management. 85 | --------------------------------------------------------------------------------