pdfalyzer
DidierStevensSuite
Our great sponsors
pdfalyzer | DidierStevensSuite | |
---|---|---|
8 | 7 | |
213 | 1,827 | |
- | - | |
6.9 | 5.6 | |
12 days ago | 18 days ago | |
Python | Python | |
GNU General Public License v3.0 only | - |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
pdfalyzer
-
Are there any PDF specific YARA rules you know of that are not collected in The Pdfalyzer repo yet?
Direct link to the folder with 3 .yara files compiling a bunch of YARA rule sources. Looking for anything not represented here, or even ideas for such.
-
The Pdfalyzer is a tool for visualizing the inner tree structure of a PDF in large and colorful diagrams as well as scanning its internals for suspicious content
The Pdfalyzer is a command line tool (paralyze) as well as a library for working with, visualizing, and scanning the contents of a PDF. Motivation for the project was personal: I got hacked by a PDF that turned out to be hiding its maleficent instructions inside the font binary where it was missed by modern malware scanners (twitter thread) (more details)
-
The Yaralyzer is a new tool for visualizing / force decoding YARA and regular expression matches in binary and text
A few weeks ago I made a post here about a PDF that evaded all malware detection and caused a security breach, almost certainly through PDF instructions hidden inside of an Adobe Type1 Font binary stream embedded within a PDF. At the time I posted a link to a tool I wrote called The Pdfalyzer that diagrams a PDF's internal and scans for various suspect content.
- Any useful cybersecurity software under $5k?
-
Novel PDF malware: injecting JavaScript into the encrypted section of Adobe Type 1 font binaries is not detectable by malware scanners and doesn't interfere with decryption/decompilation of the font (along with a new tool for malicious PDF analysis)
I dramatically scaled up the binary data scouring and visualization in the pdfalyzer... can rip through every backtick/frontslash/single or double quoted/etc etc set of bytes in the binaries and try a bunch of aggressive approaches to force decode them.
-
Novel (?) PDF attack (and a new PDF visualization/threat assessment tool): injecting JavaScript into the encrypted section of Adobe Type 1 font binaries is not detectable by malware scanners (nor does it interfere with the decryption of the font)
The tool is the the pdfalyzer; I just open sourced it. Meant to fill in some gaps around pdf-parser.py and the rest of Didier Stevens's malicious PDF toolkit. Makes pretty charts, previews binary data, and (most importantly) digs through PDF font binaries for potentially executable stuff. Example output can be seen at the GitHub link.
DidierStevensSuite
-
Request: DidierStevens, I need a simple guide on how to scan pdf's for malware, I want to specifically make sure I include/implement all of DidierStevens additions to antivirus detection/research.
Didier Stevens is a famous security researcher, but his instructions on how to scan pdf's require the terminal & many commands. I am hostile to this in general as I think it can all be implemented in a simple one click scan tool, instead. Which is what I am looking for. Here are all his relevant links & antivirus/anti-malware projects & tutorials: https://github.com/DidierStevens/DidierStevensSuite
-
The Pdfalyzer is a tool for visualizing the inner tree structure of a PDF in large and colorful diagrams as well as scanning its internals for suspicious content
This tool was built to fill a gap in the PDF assessment landscape. Didier Stevens's pdfid.py and pdf-parser.py are still the best game in town when it comes to PDF analysis tools but they lack in the visualization department and also don't give you much to work with as far as giving you a data model you can write your own code around. Peepdf seemed promising but turned out to be in a buggy, out of date, and more or less unfixable state. And neither of them offered much in the way of tooling for embedded binary analysis. Thus I felt the world might be slightly improved if I strung together a couple of more stable/well known/actively maintained open source projects (AnyTree, PyPDF2, and Rich) into this tool.
-
[EXCEL] macro recorder and macros that use other macros: any way to avoid fully qualified names?
OK then. So I won't accidentally miss any, I've made a little script to find them. It uses oledump.py which, as the name suggests is a Python script to dump OLE files. Here is oledump.py on Github. Excel stores its macros in an OLE file names xl/vbaProject.bin inside the .xlsm file, and oledump knows how to find that, list the streams in them, and extract the macros from the streams that contain them.
-
Extracting attachments from saved emails (.eml)
You can install emldump and programmatically extract all attachments
-
What's in your toolkit?
Didier Stevens Suite - He has a tool for everything.
What are some alternatives?
peepdf - Powerful Python tool to analyze PDF documents
dislocker - FUSE driver to read/write Windows' BitLocker-ed volumes under Linux / Mac OSX
pypdfium2 - Python bindings to PDFium
TheHive - TheHive: a Scalable, Open Source and Free Security Incident Response Platform
Malware-IOCs
Serpico - SimplE RePort wrIting and COllaboration tool
SysmonForLinux
treblle-node - The official Treblle SDK for NodeJS/ExpressJS. Seamlessly integrate Treblle to manage communication with your dashboard, send errors, and secure sensitive data.
CyberPipe - An easy to use PowerShell script to collect memory and disk forensics for DFIR investigations.
Feedly-Backup - Backup of my feedly... feeds
threat-tools - Tools for simulating threats
postman-app-support - Postman is an API platform for building and using APIs. Postman simplifies each step of the API lifecycle and streamlines collaboration so you can create better APIs—faster.