[PATCH v3 7/8] Documentation: Add documentation for the Brute LSM
john.wood at gmx.com
Sun Feb 21 15:49:18 UTC 2021
Add some info detailing what is the Brute LSM, its motivation, weak
points of existing implementations, proposed solutions, enabling,
disabling and self-tests.
Signed-off-by: John Wood <john.wood at gmx.com>
Documentation/admin-guide/LSM/Brute.rst | 224 ++++++++++++++++++++++++
Documentation/admin-guide/LSM/index.rst | 1 +
security/brute/Kconfig | 3 +-
3 files changed, 227 insertions(+), 1 deletion(-)
create mode 100644 Documentation/admin-guide/LSM/Brute.rst
diff --git a/Documentation/admin-guide/LSM/Brute.rst b/Documentation/admin-guide/LSM/Brute.rst
new file mode 100644
@@ -0,0 +1,224 @@
+.. SPDX-License-Identifier: GPL-2.0
+Brute: Fork brute force attack detection and mitigation LSM
+Attacks against vulnerable userspace applications with the purpose to break ASLR
+or bypass canaries traditionally use some level of brute force with the help of
+the fork system call. This is possible since when creating a new process using
+fork its memory contents are the same as those of the parent process (the
+process that called the fork system call). So, the attacker can test the memory
+infinite times to find the correct memory values or the correct memory addresses
+without worrying about crashing the application.
+Based on the above scenario it would be nice to have this detected and
+mitigated, and this is the goal of this implementation. Specifically the
+following attacks are expected to be detected:
+1.- Launching (fork()/exec()) a setuid/setgid process repeatedly until a
+ desirable memory layout is got (e.g. Stack Clash).
+2.- Connecting to an exec()ing network daemon (e.g. xinetd) repeatedly until a
+ desirable memory layout is got (e.g. what CTFs do for simple network
+3.- Launching processes without exec() (e.g. Android Zygote) and exposing state
+ to attack a sibling.
+4.- Connecting to a fork()ing network daemon (e.g. apache) repeatedly until the
+ previously shared memory layout of all the other children is exposed (e.g.
+ kind of related to HeartBleed).
+In each case, a privilege boundary has been crossed:
+Case 1: setuid/setgid process
+Case 2: network to local
+Case 3: privilege changes
+Case 4: network to local
+So, what really needs to be detected are fork/exec brute force attacks that
+cross any of the commented bounds.
+The public version of grsecurity, as a summary, is based on the idea of delaying
+the fork system call if a child died due to some fatal signal (SIGSEGV, SIGBUS,
+SIGKILL or SIGILL). This has some issues:
+Adding delays to the kernel is, in general, a bad idea.
+Scenarios not detected (false negatives)
+This protection acts only when the fork system call is called after a child has
+crashed. So, it would still be possible for an attacker to fork a big amount of
+children (in the order of thousands), then probe all of them, and finally wait
+the protection time before repeating the steps.
+Moreover, this method is based on the idea that the protection doesn't act if
+the parent crashes. So, it would still be possible for an attacker to fork a
+process and probe itself. Then, fork the child process and probe itself again.
+This way, these steps can be repeated infinite times without any mitigation.
+Scenarios detected (false positives)
+Scenarios where an application rarely fails for reasons unrelated to a real
+The main idea behind this implementation is to improve the existing ones
+focusing on the weak points annotated before. Basically, the adopted solution is
+to detect a fast crash rate instead of only one simple crash and to detect both
+the crash of parent and child processes. Also, fine tune the detection focusing
+on privilege boundary crossing. And finally, as a mitigation method, kill all
+the offending tasks involved in the attack instead of using delays.
+To achieve this goal, and going into more details, this implementation is based
+on the use of some statistical data shared across all the processes that can
+have the same memory contents. Or in other words, a statistical data shared
+between all the fork hierarchy processes after an execve system call.
+The purpose of these statistics is, basically, collect all the necessary info
+to compute the application crash period in order to detect an attack. This crash
+period is the time between the execve system call and the first fault or the
+time between two consecutive faults, but this has a drawback. If an application
+crashes twice in a short period of time for some reason unrelated to a real
+attack, a false positive will be triggered. To avoid this scenario the
+exponential moving average (EMA) is used. This way, the application crash period
+will be a value that is not prone to change due to spurious data and follows the
+real crash period.
+To detect a brute force attack it is necessary that the statistics shared by all
+the fork hierarchy processes be updated in every fatal crash and the most
+important data to update is the application crash period.
+There are two types of brute force attacks that need to be detected. The first
+one is an attack that happens through the fork system call and the second one is
+an attack that happens through the execve system call. The first type uses the
+statistics shared by all the fork hierarchy processes, but the second type
+cannot use this statistical data due to these statistics dissapear when the
+involved tasks finished. In this last scenario the attack info should be tracked
+by the statistics of a higher fork hierarchy (the hierarchy that contains the
+process that forks before the execve system call).
+Moreover, these two attack types have two variants. A slow brute force attack
+that is detected if a maximum number of faults per fork hierarchy is reached and
+a fast brute force attack that is detected if the application crash period falls
+below a certain threshold.
+Exponential moving average (EMA)
+This kind of average defines a weight (between 0 and 1) for the new value to add
+and applies the remainder of the weight to the current average value. This way,
+some spurious data will not excessively modify the average and only if the new
+values are persistent, the moving average will tend towards them.
+Mathematically the application crash period's EMA can be expressed as follows:
+period_ema = period * weight + period_ema * (1 - weight)
+Related to the attack detection, the EMA must guarantee that not many crashes
+are needed. To demonstrate this, the scenario where an application has been
+running without any crashes for a month will be used.
+The period's EMA can be written now as:
+period_ema[i] = period[i] * weight + period_ema[i - 1] * (1 - weight)
+If the new crash periods have insignificant values related to the first crash
+period (a month in this case), the formula can be rewritten as:
+period_ema[i] = period_ema[i - 1] * (1 - weight)
+And by extension:
+period_ema[i - 1] = period_ema[i - 2] * (1 - weight)
+period_ema[i - 2] = period_ema[i - 3] * (1 - weight)
+period_ema[i - 3] = period_ema[i - 4] * (1 - weight)
+So, if the substitution is made:
+period_ema[i] = period_ema[i - 1] * (1 - weight)
+period_ema[i] = period_ema[i - 2] * pow((1 - weight) , 2)
+period_ema[i] = period_ema[i - 3] * pow((1 - weight) , 3)
+period_ema[i] = period_ema[i - 4] * pow((1 - weight) , 4)
+And in a more generic form:
+period_ema[i] = period_ema[i - n] * pow((1 - weight) , n)
+Where n represents the number of iterations to obtain an EMA value. Or in other
+words, the number of crashes to detect an attack.
+So, if we isolate the number of crashes:
+period_ema[i] / period_ema[i - n] = pow((1 - weight), n)
+log(period_ema[i] / period_ema[i - n]) = log(pow((1 - weight), n))
+log(period_ema[i] / period_ema[i - n]) = n * log(1 - weight)
+n = log(period_ema[i] / period_ema[i - n]) / log(1 - weight)
+Then, in the commented scenario (an application has been running without any
+crashes for a month), the approximate number of crashes to detect an attack
+(using the implementation values for the weight and the crash period threshold)
+weight = 7 / 10
+crash_period_threshold = 30 seconds
+n = log(crash_period_threshold / seconds_per_month) / log(1 - weight)
+n = log(30 / (30 * 24 * 3600)) / log(1 - 0.7)
+n = 9.44
+So, with 10 crashes for this scenario an attack will be detected. If these steps
+are repeated for different scenarios and the results are collected:
+1 month without any crashes ----> 9.44 crashes to detect an attack
+1 year without any crashes -----> 11.50 crashes to detect an attack
+10 years without any crashes ---> 13.42 crashes to detect an attack
+However, this computation has a drawback. The first data added to the EMA not
+obtains a real average showing a trend. So the solution is simple, the EMA needs
+a minimum number of data to be able to be interpreted. This way, the case where
+a few first faults are fast enough followed by no crashes is avoided.
+Per system enabling/disabling
+This feature can be enabled at build time using the CONFIG_SECURITY_FORK_BRUTE
+option or using the visual config application under the following menu:
+Security options ---> Fork brute force attack detection and mitigation
+Also, at boot time, this feature can be disable too, by changing the "lsm=" boot
+To validate all the expectations about this implementation, there is a set of
+selftests. This tests cover fork/exec brute force attacks crossing the following
+1.- setuid process
+2.- privilege changes
+3.- network to local
+Also, there are some tests to check that fork/exec brute force attacks without
+crossing any privilege boundariy already commented doesn't trigger the detection
+and mitigation stage.
+To build the tests:
+make -C tools/testing/selftests/ TARGETS=brute
+To run the tests:
+make -C tools/testing/selftests TARGETS=brute run_tests
+To package the tests:
+make -C tools/testing/selftests TARGETS=brute gen_tar
diff --git a/Documentation/admin-guide/LSM/index.rst b/Documentation/admin-guide/LSM/index.rst
index a6ba95fbaa9f..1f68982bb330 100644
@@ -41,6 +41,7 @@ subdirectories.
diff --git a/security/brute/Kconfig b/security/brute/Kconfig
index 1bd2df1e2dec..334d7e88d27f 100644
@@ -7,6 +7,7 @@ config SECURITY_FORK_BRUTE
vulnerable userspace processes. The detection method is based on
the application crash period and as a mitigation procedure all the
offending tasks are killed. Like capabilities, this security module
- stacks with other LSMs.
+ stacks with other LSMs. Further information can be found in
If you are unsure how to answer this question, answer N.
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