GDB, Linux

Unravelling Code Injection in Binaries

It seems pretty surreal going through old lab notes again. It’s like a time capsule – an opportunity to laugh at your previous stupid self and your desperate attempts at trying to rectify that situation. My previous post on GDB’s fast tracepoints and their clever use of jump-pads longs for a more in-depth explanation on what goes on when you inject your own code in binaries.

Binary Instrumentation

The ability to inject code dynamically in a binary – either executing or on disk gives huge power to the developer. It basically eliminates the need of source code and re-compilation in most of the cases where you want to have your own small code run in a function and which may change the flow of program. For example, a tiny tracer that counts the number of time a certain variable was assigned a value of 42 in a target function. Through binary instrumentation, it becomes easy to insert such tiny code snippets for inexpensive tracing even in production system and then safely remove them once we are done – making sure that the overhead of static tracepoints is avoided as well. Though a very common task in security domain, binary instrumentation also forms a basis for debuggers and tracing tools. I think one of the most interesting study material to read from an academic perspective is Nethercote’s PhD Thesis. Through this, I learnt about the magic of Valgrind (screams for a separate blog post), the techniques beyond breakpoints and  trampolines. In reality, most of us may not usually look beyond ptrace() when we hear about playing with code instrumentation. GDB’s backbone and some of my early experiments for binary fun have been with ptrace() only. While Eli Bendersky explains some of the debugger magic and the role of ptrace() in sufficient detail, I explore more on what happens when the code is injected and it modifies the process while it executes.

Primer

The techniques for binary instrumentation are numerous. The base of all the approaches is the ability to halt the process,identify an instrumentation point (a function, an offset from function start, an address etc.), modify its memory at that point, execute code and rewrite/restore registers. For on-disk dynamic instrumentation, the ability to load the binary, parse, disassemble and patch it with instrumentation code is required. There are then multiple ways to insert/patch the binary. In all these ways, there is always a tradeoff between overhead (size and the added time due to extra code added), control over the binary (how much and where can we modify) and robustness (what if the added code makes the actual execution unreliable – for example, loops etc.). From what I have understood from my notes, I basically classify ways to go about code patching. There may be more crazy ones (such as those used in viruses) but for tracing/debugging tools most of them are as follows :

  • TRAP Based : I already discussed this in the last post with GDB’s normal tracepoints. Such a technique is also used in older non-optimized Kprobes. An exception causing instruction (such as int 3) is inserted at the desired point and its handler calls the code which you want to execute. Pretty standard stuff.
  • JIT Recompilatin Based : This is something more interesting and is used by Valgrind. The binary is first disassembled, and converted to an intermediate representation (IR). Then IR is instrumented with the analysis code from the desired Valgrind tool (such as memcheck). The code is recompiled, stored in a code-cache and executed on a ‘synthetic CPU’. This is like JIT compilation but applied to analysis tools. The control over the information that can be gathered in such cases is very high, but so is the overhead (can go from 4x-50x slower in various cases).
  • Trampoline Based : Boing! Basically, we just patch the location with a jump to a jump-pad or trampoline (different name for same thing). This trampoline can execute the displaced instructions and then prepare another jump to the instrumented code and then back to the actual code. This out-of-line execution maintains sufficient speed, reduced overhead as no TRAP, context switch or handler call is involved. Many binary instrumentation frameworks such as Dyninst are built upon this. We will explain this one in further detail.

Dyninst’s Trampoline

Dyninst’s userspace-only trampoline approach is quite robust and fast. It has been used in performance analysis tools such as SystemTap, Vampir and Tau and hence a candidate for my scrutiny. To get a feel of what happens under the hood, lets have a look at what Dyninst does to our code when it patches it.

Dyninst provides some really easy to use APIs to do the heavy lifting for you. Most of them are very well documented as well. Dyninst introduces the concept of mutator which is the program that is supposed to modify the target or mutatee. This mutatee can either be a running application or a binary residing on disk. The process attaching or creating a new target process allows the mutator to control the execution of the mutatee. This can be achieved by either processCreate() or processAttach(). The mutator then gets the program image using the object, which is a static representation of the mutatee. Using the program image, the mutator can identify all possible instrumentation points
in the mutatee. The next step is creating a snippet (or the code you want to insert) for insertion at the identified point. The mutator can then create a snippet, to be inserted into the mutatee. Building small snippets can be trivial. For example, small snippets can be defined using the BPatch arithExpr and BPatch varExp types. Here is a small sample. The snippet is compiled into machine language and copied into the application’s address space. This is easier said than done though. For now, we just care about how the snippet affects our target process.

Program Execution flow

The Dyninst API inserts this compiled snippet at the instrumentation points. Underneath is the familiar ptrace() technique of pausing and poking memory. The instruction at the instrumentation point is replaced by a jump to a base trampoline. The base trampoline then jumps to a mini-trampoline that starts by saving any registers that will be modified. Next, the instrumentation is executed. The mini-trampoline then restores the necessary registers, and jumps back to the base trampoline. Finally, the base trampoline executes the replaced instruction and jumps back to the instruction after the instrumentation point. Here is a relevant figure taken from this paper :

dyninst-working

As part of some trials, I took a tiny piece of code and inserted a snippet at the end of the function foo(). Dyninst changed it to the following :

dyninst-jmp

Hmm.. interesting. So the trampoline starts at 0x10000 (relative to PC). Our instrumentation point was intended to be function exit. It means Dyninst just replaces the whole function in this case. Probably it is safer this way rather than replacing a single or a small set of instructions mid function. Dynisnt’s API check for many other things when building the snippet. For example, we need to see if the snippet contains code that recursively calls itself causing the target program to stop going further. More like a verifier of code being inserted (similar to eBPF’s verifier in Linux kernel which checks for loops etc before allowing the eBPF bytecode execution). So what is the trampoline doing? I used GDB to hook onto what is going on and here is a reconstruction of the flow :

dyninst-mod

Clearly, the first thing the trampoline does is execute the remaining function out of line, but before returning, it start preparing the snippet’s execution. The snippet was a pre-compiled LTTng tracepoint (this is a story for another day perhaps) but you don’t have to bother much. Just think of it as a function call to my own function from within the target process. First the stack is grown and the machine registers are pushed on to the stack so that we can return to the state where we were after we have executed the instrumented code. Then it is grown further for snippet’s use. Next, the snippet gets executed (the gray box) and the stack is shrunk back again to the same amount. The registers pushed on the stack are restored along with the original stack pointer and we return as if nothing happened. There is no interrupt, no context-switch, no lengthy diversions. Just simple userspace code 🙂

So now we know! You can use Dyninst and other such frameworks like DynamoRIO or PIN to build your own tools for tracing and debugging. Playing with such tools can be insane fun as well. If you have any great ideas or want me to add something in this post, leave a comment!

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GDB, Linux

Fast Tracing with GDB

Even though GDB is a traditional debugger, it provides support for dynamic fast user-space tracing. Tracing in simple terms is super fast data logging from a target application or the kernel. The data is usually a superset of what a user would normally want from debugging but cannot get because of the debugger overhead. The traditional debugging approach can indeed alter the correctness of the application output or alter its behavior. Thus, the need for tracing arises. GDB in fact is one of the first projects which tried to have an integrated approach of debugging and tracing using the same tool. It has been designed in a manner such that sufficient decoupling is maintained – allowing it to expand and be flexible. An example is the use of In-Process Agent (IPA) which is crucial to fast tracing in GDB but is not necessary for TRAP-based normal tracepoints.

GDB’s Tracing Infrastructure

The tracing is performed by trace and collect commands. The location where the user wants to collect some data is called a tracepoint. It is just a special type of breakpoint without support of running GDB commands on a tracepoint hit. As the program progresses, and passes the tracepoint, data (such as register values, memory values etc) gets collected based on certain conditions (if desired so). The data collection is done in a trace buffer when the tracepoint is hit. Later, that data can be examined from the collected trace snapshot using tfind. However, tracing for now is restricted to remote targets (such as gdbserver). Apart from this type of dynamic tracing, there is also support for static tracepoints in which instrumentation points known as markers are embedded in the target and can be activated or deactivated.  The process of installing these static tracepoints is known as probing a marker. Considering that you have started GDB and your binary is loaded, a sample trace session may look something like this :

(gdb) trace foo
(gdb) actions
Enter actions for tracepoint #1, one per line.
> collect $regs,$locals
> while-stepping 9
  > collect $regs
  > end
> end
(gdb) tstart
[program executes/continues...]
(gdb) tstop

This puts up a tracepoint at foo, collects all register values at tracepoint hit, and then for subsequent 9 instruction executions, collects all register values. We can now analyze the data using tfind or tdump.

(gdb) tfind 0
Found trace frame 0, tracepoint 1
54    bar    = (bar & 0xFF000000) >> 24;

(gdb) tdump
Data collected at tracepoint 1, trace frame 0:
rax    0x2000028 33554472
rbx    0x0 0
rcx    0x33402e76b0 220120118960
rdx    0x1 1
rsi    0x33405bca30 220123089456
rdi    0x2000028 33554472
rbp    0x7fffffffdca0 0x7fffffffdca0
rsp    0x7fffffffdca0 0x7fffffffdca0
.
.
rip    0x4006f1 0x4006f1 <foo+7>
[and so on...]

(gdb) tfind 4
Found trace frame 4, tracepoint 1
0x0000000000400700 55    r1 = (bar & 0x00F00000) >> 20;

(gdb) p $rip
$1 = (void (*)()) 0x400700 <foo+22>

So one can observe data collected from different trace frames in this manner and even output to a separate file if required. Going more in depth to know how tracing works, lets see the GDB’s two tracing mechanisms :

Normal Tracepoints

These type of tracepoints are the basic default tracepoints. The idea of their use is similar to breakpoints where GDB replaces the target instruction with a TRAP or any other exception causing instruction. On x86, this can usually be an int 3 which has a special single byte instruction – 0xCC reserved for it. Replacing a target instruction with this 1 byte ensures that the normal instructions are not corrupted. So, during the execution of the process, the OS hits the int 3 where it halts and program state is saved. The OS sends a SIGTRAP signal to the process. As GDB is attached or is running the process, it receives a SIGCHLD as a notification, that something happened with a child. It does a wait(), which will tell it that process has received a SIGTRAP. Thus the SIGTRAP never reaches the process as GDB intercepts it. The original instruction is first restored, or executed out-of-line for non-stop multi-threaded debugging. GDB transfers the control to the trace collection which does the data collection part upon evaluating any condition set. The data is stored into a trace buffer. Then, the original instruction is replaced again with the tracepoint and normal execution continues. This all fine and good, but there is a catch – the TRAP mechanism alters the flow of the application and the control is passed to the OS which leads to some delay a compromise in speed. But even with that, because of a very restrictive conditional tracing design, and better interaction of interrupt-driven approaches with instruction caches, normal interrupt- based tracing in GDB is a robust technique. A faster solution would indeed be a pure user-space approach, where everything is done at the application level.

Fast Tracepoints

Owing to the limitations stated above, a fast tracepoint approach was developed. This special type of tracepoint uses a dynamic tracing approach. Instead of using the breakpoint approach, GDB uses a mix of IPA and remote target (gdbserver) to replace the target instruction with a 5 byte jump to a special section in memory called a jump-pad. This jump-pad, also known as a trampoline, first pushes all registers to stack (saving the program state). Then, it calls the collector  function for trace data collection, it executes the displaced instruction out-of-line, and jumps back to the original instruction stream. I will probably write something more about how trampolines work and some techniques used in dynamic instrumentation frameworks like Dyninst in a subsequent post later on.

gdb-working

Fast tracepoints can be used with command ftrace, almost exactly like with the trace command. A special command in the following format is sent to the In-Process Agent by gdbserver as,

FastTrace:<tracepoint_object><jump_pad>

where <tracepoint object> is the object containing bytecode for conditional tracing, address, type, action etc. and <jump pad> is the 8-byte long head of the jump pad location in the memory. The IPA prepares all that and if all goes well, responds to such a  query by,

OK<target_address><jump_pad><fjump_size><fjump>

where <target address> is the address where the tracepoint is put in the inferior, <jump_pad> is the updated address of the jump pad head and the <fjump> and <fjump_size> are the jump instruction sequence and its size copied to the command buffer, sent back by IPA. The remote target (gdbserver) then modifies the memory of the target process. Much more fine grained information about fast tracepoint installation is available in the GDB documentation. This is a very pure user-space approach to tracing. However, there is a catch – the target instruction to be replaced should be at least 5 bytes long for this to work as the jump is itself 5 byte long (on Intel x86). This means that fast tracepoints using GDB cannot be put everywhere. How code is modified when patching a 5 bytes instruction is a discussion of another time. This is probably the fastest way yet to perform dynamic tracing and is a good candidate for such work.

The GDB traces can be saved with tsave and with the –ctf switch may be exported to CTF also. I have not tried this but hopefully it should at least open the traces with TraceCompass for further analysis. The GDB’s fast tracepoint mechanism is quite fast I must say – but in terms of usability, LTTng is a far better and advanced option. GDB however allows dynamic insertion of tracepoints and the tracing features are well integrated in your friendly neighborhood debugger. Happy Tracing!

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