Pytm - A Pythonic Framework For Threat Modeling

Define your system in Python using the elements and properties described in the pytm framework. Based on your definition, pytm can generate, a Data Flow Diagram (DFD), a Sequence Diagram and most important of all, threats to your system.

Requirements

Linux/MacOS
Python 3.x
Graphviz package
Java (OpenJDK 10 or 11)
plantuml.jar

Usage

tm.py [-h] [--debug] [--dfd] [--report REPORT] [--exclude EXCLUDE] [--seq] [--list] [--describe DESCRIBE]    optional arguments:    -h, --help           show this help message and exit    --debug              print debug messages    --dfd                output DFD (default)    --report REPORT      output report using the named template file (sample template file is under docs/template.md)    --exclude EXCLUDE    specify threat IDs to be ignored    --seq                output sequential diagram    --list               list all available threats    --describe DESCRIBE  describe the properties available for a given element

Currently available elements are: TM, Element, Server, ExternalEntity, Datastore, Actor, Process, SetOfProcesses, Dataflow, Boundary and Lambda.
The available properties of an element can be listed by using --describe followed by the name of an element:

  (pytm) ➜  pytm git:(master) ✗ ./tm.py --describe Element  Element   OS   check   definesConnectionTimeout   description   dfd   handlesResources   implementsAuthenticationScheme   implementsNonce   inBoundary   inScope   isAdmin   isHardened   name   onAWS

For the security practitioner, you may add new threats to the threatlib/threats.json file:

tm.py [-h] [--debug] [--dfd] [--report REPORT] [--exclude EXCLUDE] [--seq] [--list] [--describe DESCRIBE]

optional arguments:
  -h, --help           show this help message and exit
  --debug              print debug messages
  --dfd                output DFD (default)
  --report REPORT      output report using the named template file (sample template file is under docs/template.md)
  --exclude EXCLUDE    specify threat IDs to be ignored
  --seq                output sequential diagram
  --list               list all available threats
  --describe DESCRIBE  describe the properties available for a given element

CAVEAT
The threats.json file contains strings that run through eval() -> make sure the file has correct permissions or risk having an attacker change the strings and cause you to run code on their behalf. The logic lives in the "condition", where members of "target" can be logically evaluated. Returning a true means the rule generates a finding, otherwise, it is not a finding.**
The following is a sample tm.py file that describes a simple application where a User logs into the application and posts comments on the app. The app server stores those comments into the database. There is an AWS Lambda that periodically cleans the Database.


(pytm) ➜  pytm git:(master) ✗ ./tm.py --describe Element
Element
 OS
 check
 definesConnectionTimeout
 description
 dfd
 handlesResources
 implementsAuthenticationScheme
 implementsNonce
 inBoundary
 inScope
 isAdmin
 isHardened
 name
 onAWS

Diagrams are output as Dot and PlantUML.
When --dfd argument is passed to the above tm.py file it generates output to stdout, which is fed to Graphviz's dot to generate the Data Flow Diagram:

{
   "SID":"INP01",
   "target": ["Lambda","Process"],
   "description": "Buffer Overflow via Environment Variables",
   "details": "This attack pattern involves causing a buffer overflow through manipulation of environment variables. Once the attacker finds that they can modify an environment variable, they may try to overflow associated buffers. This attack leverages implicit trust often placed in environment variables.",
   "Likelihood Of Attack": "High",
   "severity": "High",
   "condition": "target.usesEnvironmentVariables is True and target.sanitizesInput is False and target.checksInputBounds is False",
   "prerequisites": "The application uses environment variables.An environment variable exposed to the user is vulnerable to a buffe   r overflow.The vulnerable environment variable uses untrusted data.Tainted data used in the environment variables is not properly validated. For instance boundary checking is not done before copying the input data to a buffer.",
   "mitigations": "Do not expose environment variable to the user.Do not use untrusted data in your environment variables. Use a language or compiler that performs automatic bounds checking. There are tools such as Sharefuzz [R.10.3] which is an environment variable fuzzer for Unix that support loading a shared library. You can use Sharefuzz to determine if you are exposing an environment variable vulnerable to buffer overflow.",
   "example": "Attack Example: Buffer Overflow in $HOME A buffer overflow in sccw allows local users to gain root access via the $HOME environmental variable. Attack Example: Buffer Overflow in TERM A buffer overflow    in the rlogin program involves its consumption of the TERM environmental variable.",
   "references": "https://capec.mitre.org/data/definitions/10.html, CVE-1999-0906, CVE-1999-0046, http://cwe.mitre.org/data/definitions/120.html, http://cwe.mitre.org/data/definitions/119.html, http://cwe.mitre.org/data/definitions/680.html"
 }

Generates this diagram:

The following command generates a Sequence diagram.

#!/usr/bin/env python3

from pytm.pytm import TM, Server, Datastore, Dataflow, Boundary, Actor, Lambda

tm = TM("my test tm")
tm.description = "another test tm"

User_Web = Boundary("User/Web")
Web_DB = Boundary("Web/DB")

user = Actor("User")
user.inBoundary = User_Web

web = Server("Web Server")
web.OS = "CloudOS"
web.isHardened = True

db = Datastore("SQL Database (*)")
db.OS = "CentOS"
db.isHardened = False
db.inBoundary = Web_DB
db.isSql = True
db.inScope = False

my_lambda = Lambda("cleanDBevery6hours")
my_lambda.hasAccessControl = True
my_lambda.inBoundary = Web_DB

my_lambda_to_db = Dataflow(my_lambda, db, "(&lambda;)Periodically cleans DB")
my_lambda_to_db.protocol = "SQL"
my_lambda_to_db.dstPort = 3306

user_to_web = Dataflow(user, web, "User enters comments (*)")
user_to_web.protocol = "HTTP"
user_to_web.dstPort = 80
user_to_web.data = 'Comments in HTML or Markdown'
user_to_web.order = 1

web_to_user = Dataflow(web, user, "Comments saved (*)")
web_to_user.protocol = "HTTP"
web_to_user.data = 'Ack of saving or error message, in JSON'
web_to_user.order = 2

web_to_db = Dataflow(web, db, "Insert query with comments")
web_to_db.protocol = "MySQL"
web_to_db.dstPort = 3306
web_to_db.data = 'MySQL insert statement, all literals'
web_to_db.order = 3

db_to_web = Dataflow(db, web, "Comments contents")
db_to_web.protocol = "MySQL"
db_to_web.data = 'Results of insert op'
db_to_web.order = 4

tm.process()

Generates this diagram:

The diagrams and findings can be included in the template to create a final report:

tm.py --dfd | dot -Tpng -o sample.png

The templating format used in the report template is very simple:

  # Threat Model Sample  ***    ## System Description    {tm.description}    ## Dataflow Diagram    ![Level 0 DFD](dfd.png)    ## Dataflows    Name|From|To |Data|Protocol|Port  ----|----|---|----|--------|----  {dataflows:repeat:{{item.name}}|{{item.source.name}}|{{item.sink.name}}|{{item.data}}|{{item.protocol}}|{{item.dstPort}}  }    ## Findings    {findings:repeat:* {{item.description}} on element "{{item.target}}"  }

Currently supported threats

INP01 - Buffer Overflow via Environment Variables  INP02 - Overflow Buffers  INP03 - Server Side Include (SSI) Injection  CR01 - Session Sidejacking  INP04 - HTTP Request Splitting  CR02 - Cross Site Tracing  INP05 - Command Line Execution through SQL Injection  INP06 - SQL Injection through SOAP Parameter Tampering  SC01 - JSON Hijacking (aka JavaScript Hijacking)  LB01 - API Manipulation  AA01 - Authentication Abuse/ByPass  DS01 - Excavation  DE01 - Interception  DE02 - Double Encoding  API01 - Exploit Test APIs  AC01 - Privilege Abuse  INP07 - Buffer Manipulation  AC02 - Shared Data Manipulation  DO01 - Flooding  HA01 - Path Traversal  AC03 - Subverting Environment Variable Values  DO02 - Excessive Allocation  DS02 - Try All Common Switches  INP08 - Format String Injection  INP09 - LDAP Injection  INP10 - Parameter Injection  INP11 - Relative Path Traversal  INP12 - Client-side Injection-induced Buffer Overflow  AC04 - XML Schema Poisoning  DO03 - XML Ping of the Death  AC05 - Content Spoofing  INP13 - Command Delimiters  INP14 - Input Data Manipulation  DE03 - Sniffing Attacks  CR03 - Dictionary-based Password Attack  API02 - Exploit Script-Based APIs  HA02 - White Box Reverse Engineering  DS03 - Footprinting  AC06 - Using Malicious Files  HA03 - Web Application Fingerprinting  SC02 - XSS Targeting Non-Script Elements  AC07 - Exploiting Incorrectly Configured Access Control Security Levels  INP15 - IMAP/SMTP Command Injection  HA04 - Reverse Engineering  SC03 - Embedding Scripts within Scripts  INP16 - PHP Remote File Inclusion  AA02 - Principal Spoof  CR04 - Session Credential Falsification through Forging  DO04 - XML Entity Expansion  DS04 - XSS Targeting Error Pages  SC04 - XSS Using Alternate Syntax  CR05 - Encryption Brute Forcing  AC08 - Manipulate Registry Information  DS05 - Lifting Sensitive Data Embedded in Cache

Download Pytm

Pytm - A Pythonic Framework For Threat Modeling

Posting Komentar

APKLab - Android Reverse Engineering WorkBench For VS Code

Formulir Kontak