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Modern Post-Exploitation Strategies - 44CON 2012
The document discusses advanced post-exploitation strategies for managing long-term, complex attack engagements, emphasizing the need for more effective tooling and methodologies. It introduces a distributed implant architecture using Python to separate actions from motivations, enhance stealth, and facilitate scalability. Key takeaways include the inadequacies of current post-exploitation frameworks and the importance of innovation in penetration testing and attack strategies.
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Modern Post-Exploitation Strategies - 44CON 2012
- 2.
- 3. 3 • Going todiscuss some ongoing research in extending attack techniques • Came from real world needs of being able to manage complex, long term attack engagements for customers What are we going to discuss
- 4. 4 • Looking at: • Attack process • Attack workflow • How current tooling map to complex workflows • Pen-test vs Attack What are we going to discuss
- 5. 5 One of thethings we do is to perform highly targeted attacks for our customers Targeted attack services
- 6. 6 Ongoing engagements, think~6-12 months Long term, breadth
- 7. 7 Read CEO’s email Alter source code Goal driven
- 8. 8 • Baby stepstowards a goal Multi-stage, incremental
- 9.
- 10. 10 Recon Exploit Post-‐Exploit Time Huge focus on exploitation
- 11. 11 • What youdo after you compromise • Nothing to do with how you compromise What is post-exploitation?
- 12. 12 ‘How to makethe BEST use of the systems that have been compromised’ Or from the attacker perspective
- 13. 13 What do Iworry about at night?
- 14.
- 15. 15 Manage Many Targets Rapid Development QA & Avoid Detection Maintenance Post-exploitation challenges
- 16. 16 I also worryabout technology futures & how it may change the attack landscape Post-exploitation worries
- 17. ( From Gartner’s2012 Hype Cycle of Emerging Technologies - August 16, 2012 ) Technology trends
- 18. 18 Co$t of A3ack Shi> the Target A3ack Effec7veness Future questions
- 19. Cloud Computing Trends
- 20.
- 21. Bring your owndevice (BYOD) Trends
- 22. Social Media Trends
- 23. 23 Overall Trends • Largernumber of resources • Greater diversity in those resources • Increased inter-resource complexity/ dependency • Users relationship with technology has never been deeper Trends & attack
- 24. 24 Manage Many Targets Rapid Development QA & Avoid Detection Maintenance Post-exploitation challenges
- 25. 25 Not well, ifat all
- 26. 26 • Recognize currentpost-exploitation ‘binary dropper’ approaches don’t scale well • In the development process • In the ability to be effective against diverse targets • Pen-test frameworks use this approach - Software engineering nightmare Scale poorly
- 27. 27 • Baking incapabilities to the implant is sub- optimal for most situations • Reduce your flexibility post-compromise • Can reveal an overall attack intent - Reverse engineering field day Baking in
- 28. 28 BIG is not what should be most worrying Small is Size matters
- 29. 29 Scalability Greater platform independence Easier to develop & maintain logic Stealth Reduced attribution & MO leakage Avoid existing deployed defenses High Level Aims
- 30.
- 31. 31 • Would begreat to have a single payload that would run everywhere! • Cross platform, Interpreted Languages such as Java or Python could help here • They also help address some of the software engineering worries Goals
- 32. 32 • Separate whatyou do, from why you do it • Lots of distributed system approaches that may help out here e.g. RPC • Can also help with reducing complexity in the implant, pushing it to the server Goals
- 33. 33 • Uses Pythonover-the-wire bytecode for cross-platform tasking • No persistent native binary code • Harder analysis on both platter & wire • A distributed implant architecture, RPC based • Split the task & the decision • ‘Reach back’ rather than ‘bake in’ The implementation
- 34. 34 Post-exploitation logic executesin the the Interpreted Language runtime, not on the target platform RPC Server IL implant Bytecode IL bytecode Dispatch IL Process Task Loop Tasks process Return IL source Process Object High level
- 35. 35 Python internals 101 Python Internals 101
- 36. 36 Bytecode • Python sourcecode as written by the programmer is compiled to a simple bytecode representation This is what the .pyc’s/.pyo’s are • Python bytecode is portable between platforms & architectures As long as major & minor versions are the same (micro can vary) Python internals 101
- 37. 37 Import hooks • Pythonhas modules & packages • import statement is used to access them & resolve their dependency tree • An import hook is custom importer that can be used to find & load modules in non-standard ways Importer protocol defined in PEP302 Python internals 101
- 38. 38 • Writing newhooks can be a pain in the *** Worth a whole talk in itself, see ‘Import this, that and the other thing’ by Brett Cannon PyCon2010 – it’s excellent • Python 3.x reduces this pain via importlib • Not available in Python 2.x so you need to implement from scratch using PEP 302 Available since v2.3 to better customize the __import__ builtin Given 2.x is in the widest use this is what I did Python internals 101
- 39. 39 • The PEP302 protocol defines A Finder Tends to be pretty straightforward Locate the module/package code A Loader More complex Compile to bytecode if needed Insert module into namespace Execute top level code Lots of annoying metadata bookkeeping Python internals 101
- 40.
- 41. 41 • Self-Bootstrapping Native task • Stage 0 is the only Tasking Binary Injector / persistent part of the bytecode Userland Exec implant. Tiny & generic Stage 2 RPC Import Hook • Simple event-loop that & Mainloop GETs bytecode over SSL & Stage 0 Stage 1 runs it from memory Bytecode HTTPS + Exec ZIP Import (Persistent) Hook • This is used to bootstrap PythonVM the Stage 1 import hook ….
- 42. 42 • Stage 1 Import Hook - Native task In memory import of a zip over SSL Binary Tasking Injector / bytecode Userland Exec • Zip imports supported since Py2.3 Stage 2 • but only from the filesystem not RPC Import Hook & Mainloop memory Stage 0 Bytecode Stage 1 HTTPS + • Re-implement the stdlib Exec (Persistent) ZIP Import Hook zipfile module in Python PythonVM
- 43. 43 (SSL) Bootstrap Get Zip Stage 1 Stage 0 server Unzip Expanded zip Zip in in memory memory Custom Finder Import Import Hook in Stage 1 Loader Module / Package in frame’s namespace Stage 1
- 44. 44 Native task • Stage 2 is a full RPC Import Hook + RPC client node Binary Tasking bytecode Injector / Userland • Import hook resolves Exec bytecode dependency trees Stage 2 remotely & transparently RPC Import Hook & Mainloop • No sourcecode mods Stage 0 Bytecode Stage 1 HTTPS + • Fully symmetric RPC system Exec (Persistent) ZIP Import Hook over SSL PythonVM • Splits the task & decision
- 45. 45 RPC Server Endpoint Implant Remote HTTPS Finder Import RPC Map into mem Compile & Strip Pre- Loader Sys.modules compiled Payload Stdlib Cache Scrub mem Stage 2 RPC import hook
- 46. 46 • Now thereis the ability for complex bytecode bundles to be sent, executed and automatically have dependencies resolved remotely without touching disk Write completely standard Python Much quicker to write than C/ASM Much easier to debug/QA Non-stdlib packages easily usable Stage 2 Mainloop
- 47. 47 • 1st Taskperformed is to derive a UUID IP’s are often used but generally a bad choice when managing many targets • Instead we use SYSUUID from SMBIOS Fairly easy to get at from Pure Python on Unixes, Linux & OSX Pain in the a** on Windows but can be done via Ctypes Initialization
- 48. 48 • The implantuses a polling mechanism rather than a persistent connection At random intervals checks-in to RPC endpoint(s) Pending tasks can be sent as A task ID to import, resolve & execute All tasks can operate in own thread or child • Nothing needs to touch disk • Result objects cached & returned next check-in Mainloop
- 49. 49 RPC UUID: Result Objects Endpoint Poll Loop Result Cache Result Obj UUID Result Payload bytecode Spawn New Processing Dispatcher Task RPC Logic To run/import Endpoint Service New task RPC Logic imports New Task Services Task Services Process Services Queue Mainloop
- 50. 50 • Tasks aresplit into 2 parts Payload: What executes on the target Service: The logic that processes the result of the payload, executes on the server • Payloads are pure Python bytecode • Determination of next task happens at the server If compromise detected we leak minimal MO Allows easy updating of goal oriented logic No need to define goal at asset creation time Tasks
- 51. 51 • A Common Task is one that Native task is pure Python bytecode E.g. Search for files named Common Binary ‘pk.pem’ Injector / Task Bytecode Userland Exec • There is a balance to be struck between stealth & Stage 2 RPC Import Hook efficiency when splitting & Mainloop tasks Task searching for ‘secret.doc’ Stage 0 Stage 1 Bytecode HTTPS + can leak MO Exec ZIP Import Exfiltrating every filename to (Persistent) Hook match to ‘secret.doc’ at the PythonVM server would use bandwidth
- 52. 52 • A Native Task is one that Native task executes native code Some tasks are too low level/ Binary specific for Python Tasking bytecode Injector / Userland • A number of options Exec depending on OS Stage 2 Ctypes, PyObjC, Subprocess RPC Import Hook & Mainloop • Potential issues Forensically noisy Stage 0 Bytecode Stage 1 HTTPS + Native functions may Exec (Persistent) ZIP Import Hook be hooked • One solution userland PythonVM execution …….
- 53. 53 • Allows executionthrough the replacement/modification of existing process image with a new one Without calling OS (Execve, loadlibrary etc) Without having to load from disk • Useful in a number of scenarios Antiforensics Non-exec filesystem mounts Wanting to inject native code from a IL VM! Userland execution
- 54. 54 • Builds onyears of other people research • Grugqs Phrack 62 paper ul_exec & FIST (Linux) • Pluf & Ripe’s SELF work from Phrack 63 (Linux) • Immunity’s PyELF library (Linux) • Nebbet’s Shuttle (Windows) • Dai Zovi’s & Iozzo’s Mach-O work (OS X) • pyMachO … Userland execution
- 55. 55 • Facilitates userlandexec from a Python runtime on OS X Think PyELF for OS X Nicely sidesteps code-signing controls • Send a Mach-O binary over the wire to a Python userland exec task, & inject it into an existing process pyMachO
- 56. 56 Native Binary Inject Python Userland Exec (pyMachO, pyELF….) Over the wire Python Implant Layer Native Bytecode (RPC) Binary Data Task Python Runtime pyMachO
- 57. 57 • For thedemo we will inject an OSX MachO bundle to do webcam capture • isight.bundle hasn’t worked since 64bit Snow Leopard • Relies on Quicktime.framework • 32 bit only • So we wrote a new one for the demo using QTKit (32 & 64 bit supported) Example injection
- 58. Implant Server 58 Webcam Grab Binary Facial Recognition pyMachO ? Tasking Get SysUUID Stage 2 RPC Hook Stage 1 HTTP/Zip Hook Bootstrap Stage 0 (persistent) Python VM Tying it all together
- 59.
- 60. 60 Takeaways • Current post-exploitationapproaches do not scale well • Baking-in capabilities can leak your intent • Interpreted languages can help with scale • Distributed architectures can help with separating action from reason Summary
- 61. 61 Calls to action Providers • Don’t let the current toolsets dictate and limit you, critique, innovate & change them to suit your needs Customers • Understand the difference between, and value of Pen-Testing vs Attack Teaming Summary
- 62. 62 ¿ Rich@Kyr.us Rich@Kyr.us Questions