Tai-e – An Easy-To-Learn/Use Static Analysis Framework For Java

Tai-e – An Easy-To-Learn/Use Static Analysis Framework For Java



What is Tai-e?

Tai-e (Chinese: 太阿; pronunciation: [ˈtaɪə:]) is a new static analysis framework for Java (please see our technical report for details), which features arguably the “best” designs from both the novel ones we proposed and those of classic frameworks such as Soot, WALA, Doop, and SpotBugs. Tai-e is easy-to-learn, easy-to-use, efficient, and highly extensible, allowing you to easily develop new analyses on top of it.

Currently, Tai-e provides the following major analysis components (and more analyses are on the way):

  • Powerful pointer analysis framework
    • On-the-fly call graph construction
    • Various classic and advanced techniques of heap abstraction and context sensitivity for pointer analysis
    • Extensible analysis plugin system (allows to conveniently develop and add new analyses that interact with pointer analysis)
  • Various fundamental/client/utility analyses
    • Fundamental analyses, e.g., reflection analysis and exception analysis
    • Modern language feature analyses, e.g., lambda and method reference analysis, and invokedynamic analysis
    • Clients, e.g., configurable taint analysis (allowing to configure sources, sinks and taint transfers)
    • Utility tools like analysis timer, constraint checker (for debugging), and various graph dumpers
  • Control/Data-flow analysis framework
    • Control-flow graph construction
    • Classic data-flow analyses, e.g., live variable analysis, constant propagation
    • Your data-flow analyses
  • SpotBugs-like bug detection system
    • Bug detectors, e.g., null pointer detector, incorrect clone() detector
    • Your bug detectors

Tai-e is developed in Java, and it can run on major operating systems including Windows, Linux, and macOS.

How to Obtain Runnable Jar of Tai-e?

The simplest way is to download it from GitHub Releases.

Alternatively, you might build the latest Tai-e yourself from the source code. This can be simply done via Gradle (be sure that Java 17 (or higher version) is available on your system). You just need to run command gradlew fatJar, and then the runnable jar will be generated in tai-e/build/, which includes Tai-e and all its dependencies.

Documentation

We are hosting the documentation of Tai-e on the GitHub wiki, where you could find more information about Tai-e such as Setup in IntelliJ IDEA , Command-Line Options , and Development of New Analysis .

Tai-e Assignments

In addition, we have developed an educational version of Tai-e where eight programming assignments are carefully designed for systematically training learners to implement various static analysis techniques to analyze real Java programs. The educational version shares a large amount of code with Tai-e, thus doing the assignments would be a good way to get familiar with Tai-e.

Kscan – Simple Asset Mapping Tool



0 Disclaimer (The author did not participate in the XX action, don’t trace it)

  • This tool is only for legally authorized enterprise
    security construction behaviors and personal learning behaviors. If you
    need to test the usability of this tool, please build a target drone
    environment by yourself.

  • When using this tool for testing, you should ensure that
    the behavior complies with local laws and regulations and has obtained
    sufficient authorization. Do not scan unauthorized targets.

We reserve the right to pursue your legal responsibility if the above prohibited behavior is found.

If you have any illegal behavior in the process of using
this tool, you shall bear the corresponding consequences by yourself,
and we will not bear any legal and joint responsibility.

Before installing and using this tool, please be sure to carefully read and fully understand the terms and conditions.

Unless you have fully read, fully understood and accepted
all the terms of this agreement, please do not install and use this
tool. Your use behavior or your acceptance of this Agreement in any
other express or implied manner shall be deemed that you have read and
agreed to be bound by this Agreement.

1 Introduction

 _   __
|#| /#/ Lightweight Asset Mapping Tool by: kv2	
|#|/#/   _____  _____     *     _   _
|#.#/   /Edge/ /Forum|   /#   |# |#|
|##|   |#|___  |#|      /###  |##|#|
|#.#   #####|#|     /#/_# |#.#.#|
|#|# /___|#||#|____/#/####|#|##|
|#| #\#####/ #####/#/     ##| #|

Kscan is an asset mapping tool that can perform port
scanning, TCP fingerprinting and banner capture for specified assets,
and obtain as much port information as possible without sending more
packets. It can perform automatic brute force cracking on scan results,
and is the first open source RDP brute force cracking tool on the go
platform.

2 Foreword

At present, there are actually many tools for asset
scanning, fingerprint identification, and vulnerability detection, and
there are many great tools, but Kscan actually has many different ideas.

  • Kscan hopes to accept a variety of input formats, and
    there is no need to classify the scanned objects before use, such as IP,
    or URL address, etc. This is undoubtedly an unnecessary workload for
    users, and all entries can be normal Input and identification. If it is a
    URL address, the path will be reserved for detection. If it is only
    IP:PORT, the port will be prioritized for protocol identification.
    Currently Kscan supports three input methods
    (-t,–target|-f,–fofa|–spy).

  • Kscan does not seek efficiency by comparing port numbers
    with common protocols to confirm port protocols, nor does it only detect
    WEB assets. In this regard, Kscan pays more attention to accuracy and
    comprehensiveness, and only high-accuracy protocol identification , in
    order to provide good detection conditions for subsequent application
    layer identification.

  • Kscan does not use a modular approach to do pure function
    stacking, such as a module obtains the title separately, a module
    obtains SMB information separately, etc., runs independently, and
    outputs independently, but outputs asset information in units of ports,
    such as ports If the protocol is HTTP, subsequent fingerprinting and
    title acquisition will be performed automatically. If the port protocol
    is RPC, it will try to obtain the host name, etc.

3 Compilation Manual

Compiler Manual

4 Get started

Kscan currently has 3 ways to input targets

  • -t/–target can add the –check parameter to fingerprint only the
    specified target port, otherwise the target will be port scanned and
    fingerprinted
IP address: 114.114.114.114
IP address range: 114.114.114.114-115.115.115.115
URL address: https://www.baidu.com
File address: file:/tmp/target.txt
  • –spy can add the –scan parameter to perform port scanning and
    fingerprinting on the surviving C segment, otherwise only the surviving
    network segment will be detected
[Empty]: will detect the IP address of the local machine and detect the B segment where the local IP is located
[all]: All private network addresses (192.168/172.32/10, etc.) will be probed
IP address: will detect the B segment where the specified IP address is located
  • -f/–fofa can add –check to verify the survivability of the
    retrieval results, and add the –scan parameter to perform port scanning
    and fingerprint identification on the retrieval results, otherwise only
    the fofa retrieval results will be returned
fofa search keywords: will directly return fofa search results

5 Instructions

usage: kscan [-h,--help,--fofa-syntax] (-t,--target,-f,--fofa,--spy) [-p,--port|--top] [-o,--output] [-oJ] [--proxy] [--threads] [--path] [--host] [--timeout] [-Pn] [-Cn] [-sV] [--check] [--encoding] [--hydra] [hydra options] [fofa options]


optional arguments:
  -h , --help     show this help message and exit
  -f , --fofa Get the detection object from fofa, you need to configure the environment variables in advance: FOFA_EMAIL, FOFA_KEY
  -t , --target Specify the detection target:
                  IP address: 114.114.114.114
                  IP address segment: 114.114.114.114/24, subnet mask less than 12 is not recommended
                  IP address range: 114.114.114.114-115.115.115.115
                  URL address: https://www.baidu.com
                  File address: file:/tmp/target.txt
  --spy network segment detection mode, in this mode, the internal network segment reachable by the host will be automatically detected. The acceptable parameters are:
                  (empty), 192, 10, 172, all, specified IP address (the IP address B segment will be detected as the surviving gateway)
  --check Fingerprinting the target address, only port detection will not be performed
  --scan will perform port scanning and fingerprinting on the target objects provided by --fofa and --spy
  -p , --port scan the specified port, TOP400 will be scanned by default, support: 80, 8080, 8088-8090
  -eP, --excluded-port skip scanning specified ports,support:80,8080,8088-8090
  -o , --output save scan results to file
  -oJ save the scan results to a file in json format
  -Pn After using this parameter, intelligent survivability detection will not be performed. Now intelligent survivability detection is enabled by default to improve efficiency.
  -Cn With this parameter, the console output will not be colored.
  -sV After using this parameter, all ports will be probed with full probes. This parameter greatly affects the efficiency, so use it with caution!
  --top Scan the filtered common ports TopX, up to 1000, the default is TOP400
  --proxy set proxy (socks5|socks4|https|http)://IP:Port
  --threads thread parameter, the default thread is 100, the maximum value is 2048
  --path specifies the directory to request access, only a single directory is supported
  --host specifies the header Host value for all requests
  --timeout set timeout
  --encoding Set the terminal output encoding, which can be specified as: gb2312, utf-8
  --match returns the banner to the asset for retrieval. If there is a keyword, it will be displayed, otherwise it will not be displayed
  --hydra automatic blasting support protocol: ssh, rdp, ftp, smb, mysql, mssql, oracle, postgresql, mongodb, redis, all are enabled by default
hydra options:
   --hydra-user custom hydra blasting username: username or user1,user2 or file:username.txt
   --hydra-pass Custom hydra blasting password: password or pass1,pass2 or file:password.txt
                  If there is a comma in the password, use , to escape, other symbols do not need to be escaped
   --hydra-update Customize the user name and password mode. If this parameter is carried, it is a new mode, and the user name and password will be added to the default dictionary. Otherwise the default dictionary will be replaced.
   --hydra-mod specifies the automatic brute force cracking module: rdp or rdp, ssh, smb
fofa options:
   --fofa-syntax will get fofa search syntax description
   --fofa-size will set the number of entries returned by fofa, the default is 100
   --fofa-fix-keyword Modifies the keyword, and the {} in this parameter will eventually be replaced with the value of the -f parameter

The function is not complicated, the others are explored by themselves

6 Demo

6.2 Survival network segment detection

6.3 Fofa result retrieval

6.4 Brute-force cracking

6.5 CDN identification

7 Special thanks

DotDumper – An Automatic Unpacker And Logger For DotNet Framework Targeting Files

DotDumper – An Automatic Unpacker And Logger For DotNet Framework Targeting Files



The minimal requirement to run a given sample, is to provide the “-file” argument, along with a file name or file path. If a full path is given, it is used. If a file name is given, the current working directory is checked, as well as the folder of DotDumper’s executable location.

Unless a directory name is provided, the “-log” folder name is set equal to the file name of the sample without the extension (if any). The folder is located in the same folder as DotDumper resides in, which is where the logs and dumped files will be saved in.

In the case of a library, or an alternative entry point into a binary, one must override the entry point using “-overrideEntry true”. Additionally, one has to provide the fully qualified class, which includes the name space using “-fqcn My.NameSpace.MyClass”. This tells DotDumper which class to select, which is where the provided function name (using “-functionName MyFunction”) is retrieved.

If the selected function requires arguments, one has to provide the number of arguments using “-argc” and the number of required arguments. The argument types and values are to be provided as “string|myValue int|9”. Note that when spaces are used in the values, the argument on the command-line interface needs to be encapsulated between quotes to ensure it is passed as a single argument.

Other less frequently used options such as “-raceTime” or “-deprecated” are safe in their default settings but might require tweaking in the future due to changes in the DotNet Framework. They are currently exposed in the command-line interface to easily allow changes, if need be, even if one is using an older version of DotDumper when the time comes.

First, the local system time is given, together with the original function’s return type, name, and argument(s). Second, the stack trace is given, where it shows that the sample’s main function leads to a constructor, initialises the components, and calls two custom functions. The Assembly.Load function was called from within “NavigationLib.TaskEightBestOil.GGGGGGGGGGGGGGGGGGGG(String str)”. This provides context for the analyst to find the code around this call if it is of interest.

Then, information regarding the assembly call order is given. The more stages are loaded, the more complex it becomes to see via which stages the call came to be. One normally expects one stage to load the next, but in some cases later stages utilize previous stages in a non-linear order. Additionally, information regarding the originating assembly is given to further enrich the data for the analyst.

Next, the parent hash is given. The parent of a stage is the previous stage, which in this example is not yet present. The newly loaded stage will have this stage as its parent. This allows the analyst to correlate events more easily.

Finally, the function’s return type and value are stored, along with the type, name, and value of each argument that is passed to the hooked function. If any variable is larger than 100 bytes in size, it is stored on the disk instead. A reference is then inserted in the log to reference the file, rather than showing the value. The threshold has been set to avoid hiccups in the printing of the log, as some arrays are thousands of indices in size.

The function name in the first line is not an internal function of the DotNet Framework, but rather a call to a specific function in the second stage. The types and names of the three arguments are listed in the function signature. Their values can be found in the function argument information section. This would allow an analyst to load the second stage in a custom loader with the given values for the arguments, or even do this using DotDumper by loading the previously dumped stage and providing the arguments.

Knowing what hooks are is essential to understand what managed hooks are. Managed code is executed in a virtual and managed environment, such as the DotNet runtime or Java’s virtual machine. Obtaining the memory address where the managed function resides differs from an unmanaged language such as C. Once the correct memory addresses for both functions have been obtained, the hook can be set by directly accessing memory using unsafe C#, along with DotNet’s interoperability service to call native

DotDumper is under constant review and development, all of which is focused on two main areas of interest: bug fixing and the addition of new features. During the development, the code was tested, but due to injection of hooks into the DotNet Framework’s functions which can be subject to change, it’s very well possible that there are bugs in the code. Anyone who encounters a bug is urged to open an issue on the GitHub repository, which will then be looked at. The suggestion of new features is also possible via the GitHub repository. For those with a GitHub account, or for those who rather not publicly interact, feel free to send me a private message on my Twitter.

Needless to say, if you’ve used DotDumper during an analysis, or used it in a creative way, feel free to reach out in public or in private! There’s nothing like hearing about the usage of a home-made tool!

There is more in store for DotDumper, and an update will be sent out to the community once it is available!

Fuzzable – Framework For Automating Fuzzable Target Discovery With Static Analysis



Framework for Automating Fuzzable Target Discovery with Static Analysis.

Introduction

Vulnerability researchers conducting security assessments on software will often harness the capabilities of coverage-guided fuzzing through powerful tools like AFL++ and libFuzzer. This is important as it automates the

Usage

fuzzable comes with various options to help better tune your analysis. More will be supported in future plans and any feature requests made.

Static Analysis Heuristics

To determine fuzzability, fuzzable utilize several heuristics to determine which targets are the most viable to target for dynamic analysis. These heuristics are all weighted differently using the scikit-criteria library, which utilizes multi-criteria decision analysis to determine the best candidates. These metrics and are there weights can be seen here:

Heuristic Description Weight
Fuzz Friendly Name Symbol name implies behavior that ingests file/buffer input 0.3
Risky Sinks Arguments that flow into risky calls (ie memcpy) 0.3
Natural Loops Number of loops detected with the dominance frontier 0.05
Cyclomatic Complexity Complexity of function target based on edges + nodes 0.05
Coverage Depth Number of callees the target traverses into 0.3

As mentioned, check out the technical blog post for a more in-depth look into why and how these metrics are utilized.

Many metrics were largely inspired by Vincenzo Iozzo’s original work in 0-knowledge fuzzing.

Every targets you want to analyze is diverse, and fuzzable will not be able to account for every edge case behavior in the program target. Thus, it may be important during analysis to tune these weights appropriately to see if different results make more sense for your use case. To tune these weights in the CLI, simply specify the --score-weights argument:

$ fuzzable analyze <TARGET> --score-weights=0.2,0.2,0.2,0.2,0.2

Analysis Filtering

By default, fuzzable will filter out function targets based on the following criteria:

  • Top-level entry calls – functions that aren’t called by any other calls in the target. These are ideal entry points that have potentially very high coverage.
  • Static calls(source only) functions that are static and aren’t exposed through headers.
  • Imports(binary only) other library dependencies being used by the target’s implementations.

To see calls that got filtered out by fuzzable, set the --list_ignored flag:

$ fuzzable analyze --list-ignored <TARGET>

In Binary Ninja, you can turn this setting in Settings > Fuzzable > List Ignored Calls.

In the case that fuzzable falsely filters out important calls that should be analyzed, it is recommended to use --include-* arguments to include them during the run:

# include ALL non top-level calls that were filtered out
$ fuzzable analyze --include-nontop <TARGET>

# include specific symbols that were filtered out
$ fuzzable analyze --include-sym <SYM> <TARGET>

In Binary Ninja, this is supported through Settings > Fuzzable > Include non-top level calls and Symbols to Exclude.

Harness Generation

Now that you have found your ideal candidates to fuzz, fuzzable will also help you generate fuzzing harnesses that are (almost) ready to instrument and compile for use with either a file-based fuzzer (ie. AFL++, Honggfuzz) or in-memory fuzzer (libFuzzer). To do so in the CLI:

If this target is a source codebase, the generic source template will be used.

If the target is a binary, the generic black-box template will be used, which ideally can be used with a fuzzing emulation mode like AFL-QEMU. A copy of the binary will also be created as a shared object if the symbol isn’t exported directly to be dlopened using LIEF.

At the moment, this feature is quite rudimentary, as it simply will create a standalone C++ harness populated with the appropriate parameters, and will not auto-generate code that is needed for any runtime behaviors (ie. instantiating and freeing structures). However, the templates created for fuzzable should get still get you running quickly. Here are some ambitious features I would like to implement down the road:

  • Full harness synthesis – harnesses will work directly with absolutely no manual changes needed.
  • Synthesis from potential unit tests using the DeepState framework (Source only).
  • Immediate deployment to a managed continuous fuzzing fleet.

Exporting Reports

fuzzable supports generating reports in various formats. The current ones that are supported are JSON, CSV and Markdown. This can be useful if you are utilizing this as part of automation where you would like to ingest the output in a serializable format.

In the CLI, simply pass the --export argument with a filename with the appropriate extension:

$ fuzzable analyze --export=report.json <TARGET>

In Binary Ninja, go to Plugins > Fuzzable > Export Fuzzability Report > ... and select the format you want to export to and the path you want to write it to.

Contributing

This tool will be continuously developed, and any help from external mantainers are appreciated!

  • Create an issue for feature requests or bugs that you have come across.
  • Submit a pull request for fixes and enhancements that you would like to see contributed to this tool.

License

Fuzzable is licensed under the MIT License.

Top 20 Most Popular Hacking Tools in 2022

Top 20 Most Popular Hacking Tools in 2022



As last year, this year we made a ranking with the most popular tools between January and December 2022.
Without going into further details, we have prepared a useful list of the most popular tools in Kitploit 2022:

  1. Zphisher – Automated Phishing Tool
  2. CiLocks – Android LockScreen Bypass

  3. Arkhota – A Web Brute Forcer For Android
  4. GodGenesis – A Python3 Based C2 Server To Make Life Of Red Teamer A Bit Easier. The Payload Is Capable To Bypass All The Known Antiviruses And Endpoints
  5. AdvPhishing – This Is Advance Phishing Tool! OTP PHISHING
  6. Modded-Ubuntu – Run Ubuntu GUI On Your Termux With Much Features
  7. Android-PIN-Bruteforce – Unlock An Android Phone (Or Device) By Bruteforcing The Lockscreen PIN
  8. Android_Hid – Use Android As Rubber Ducky Against Another Android Device
  9. Cracken – A Fast Password Wordlist Generator, Smartlist Creation And Password Hybrid-Mask Analysis Tool
  10. HackingTool – ALL IN ONE Hacking Tool For Hackers
  11. Arbitrium-RAT – A Cross-Platform, Fully Undetectable Remote Access Trojan, To Control Android, Windows And Linux
  12. Weakpass – Rule-Based Online Generator To Create A Wordlist Based On A Set Of Words
  13. Geowifi – Search WiFi Geolocation Data By BSSID And SSID On Different Public Databases
  14. BITB – Browser In The Browser (BITB) Templates
  15. Blackbird – An OSINT Tool To Search For Accounts By Username In 101 Social Networks
  16. Espoofer – An Email Spoofing Testing Tool That Aims To Bypass SPF/DKIM/DMARC And Forge DKIM Signatures
  17. Pycrypt – Python Based Crypter That Can Bypass Any Kinds Of Antivirus Products
  18. Grafiki – Threat Hunting Tool About Sysmon And Graphs
  19. VLANPWN – VLAN Attacks Toolkit
  20. linWinPwn – A Bash Script That Automates A Number Of Active Directory Enumeration And Vulnerability Checks

Happy New Year wishes the KitPloit team!