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, "(λ)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