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eBPF for security: a beginner's guide

Red Canary uses eBPF to gather security telemetry directly from the Linux kernel. Now you can too.

Rafael Ortiz
Originally published . Last modified .

Red Canary has started to incorporate eBPF to our Linux sensor. We wanted to explain, at a high level, what eBPF is and how it helps us protect our customers. We’ll start by describing the shortcomings we’ve experienced in gathering security telemetry and then explain how eBPF helps us solve these shortcomings. We’ll close by reviewing some of the challenges we faced in building eBPF support into our MDR product, and how we overcame them. We expect to offer Red Canary customers full eBPF telemetry support in the coming months, which will be announced in upcoming release notes.

What’s the problem?

In order to detect suspicious or malicious events, we need to gather a lot of telemetry from a running system. We use this telemetry to understand what system calls are happening, what processes are running, and how the system is behaving. Some examples of telemetry we gather are process start events, network connections, and namespace changes. There are many ways we can gather this information on a Linux system, but they are not all created equal. For example, we can gather information on processes opening files by regularly scanning procfs for open file descriptors. However, depending on our intervals, we might miss files that are opened and closed quickly. Or we could note down the file descriptor, only to have it point at a different file by the time we read it.

The ideal place to gather information on these events is directly inside the kernel. Traditionally, this can be done with the Linux Auditing subsystem or with a Linux kernel module (LKM). An alternative that’s quickly gaining traction is to gather this telemetry with eBPF, which excels at high-performance kernel instrumentation and improved observability.

What is eBPF and why is it useful?

Berkeley Packet Filter is a Linux kernel subsystem that allows a user to run a limited set of instructions on a virtual machine running in the kernel. It is divided between classic BPF (cBPF) and extended BPF (eBPF, or simply BPF). The older cBPF was limited to observing packet information, while the newer eBPF is much more powerful, allowing a user to do things such as modify packets, change syscall arguments, modify userspace applications, and more.

Safer than kernel modules

Why is this useful? Because normally if we want to run arbitrary code in the kernel, we would need to load in a kernel module. Putting aside the security implications for a moment, running code in the kernel is dangerous and error prone. If you make a mistake in a normal application, it crashes. If you make a mistake in kernel code, the computer crashes. Security is about managing business risk, so a security tool isn’t very useful if it brings down production. BPF offers us a safe alternative, while providing nearly the same amount of power. You can run arbitrary code in a kernel sandbox and collect information without the risk of breaking the host.

You can also think of BPF as a web application, whereas a kernel module is a standalone application. Which one do you trust more: visiting a website or downloading and running an application? Visiting a website is safer; a web application runs in a sandbox and can’t easily do as much damage to your machine as a downloaded application.

More efficient than AuditD

This post gives an excellent overview of AuditD’s strengths and weaknesses, but let’s compare it directly against BPF.

AuditD is relatively slow when it comes to collecting information, and incurs a non-negligible performance penalty on the system under audit. BPF offers us a significant performance advantage: we can perform some filtering, collection, and analysis within the kernel. Moving information from inside the kernel to outside the kernel is a relatively slow process (details of which are outside the scope of this post). The more work, collection, and analysis we can do inside the kernel, the faster our system will run.

AuditD is also relatively inflexible, whereas BPF gives us great flexibility. AuditD telemetry is limited to the events that the tool is designed to generate, and what we can configure it to tell us about. With BPF, we can instrument and inspect any point in the kernel we want to. We can look at specific code paths, examine function arguments, and generally collect as much information as we need to inform decision making.

BPF also allows many simultaneous consumers, allowing us to happily live alongside any other programs that take advantage of BPF. By contrast, AuditD can only be used by one program at a time. Once events are consumed from AuditD, they’re gone.

With BPF, we can look at specific code paths within the kernel, examine function arguments, and generally collect as much information as we need to inform decision making.

How do I collect telemetry from eBPF?

In order to get security telemetry from BPF, we need two main components:

  1. the BPF programs themselves, to gather information from the kernel and expose it in a useful format
  2. a way to load and interact with these BPF programs

Red Canary’s Research & Development team has built and released both of these components as free open source software. With these components in place, anyone can start to move away from relying on AuditD and Linux kernel modules to gather security telemetry.

Red Canary’s eBPF sensor

The redcanary-ebpf-sensor is the set of BPF programs that actually gather security relevant event data from the Linux kernel. The BPF programs are combined into a single ELF file from which we can selectively load individual probes, depending on the operating system and kernel version we’re running on. The probes insert themselves at various points in the kernel (such as the entrypoint and return of the execve system call) and gather information on the call and its context. This information is then turned into a telemetry event, which is sent to userspace through a perf buffer.

By having multiple probes in the same ELF binary, we can take advantage of newer kernel features (such as the read_str family of BPF functions), or probe newer syscalls (such as clone3) while retaining backwards compatibility with older kernels. This lets us build a Compile-Once, Run-Most-Places BPF sensor package.

oxidebpf

oxidebpf is a Rust library that manages eBPF programs. The goal of oxidebpf is to provide a simple interface for managing multiple BPF program versions in a Compile-Once, Run-Most-Places way. For example, here’s how easy it is to build a probe that attaches to clone3 and clone, but only if clone3 exists on the target system.

let mut program_group = ProgramGroup::new(None);

program_group.load(
    program_blueprint,
    vec![ProgramVersion::new(vec![
        Program::new(
            "test_program_clone",
            &["sys_clone"],
        )
        .syscall(true),
        Program::new(
            "test_program_clone3",
            &["sys_clone3"],
        )
        .optional(true)
        .syscall(true),
    ])]
)?;

Read our blog post for a more detailed overview of oxidebpf, along with a tutorial.

Coming soon: a GPS for the Linux kernel

One last thing we need to achieve Run-Most-Places is kernel offsets. To get some of the information we want out of the kernel, we need to pull that information out of kernel data structures. Unfortunately, these structures are not guaranteed to form a stable application binary interface (ABI) and can vary across kernel versions and distributions. The typical way to solve this is to build your BPF program on the host you’re targeting and grab information addresses locally. Unfortunately, that’s not great for ephemeral systems, short-lived systems, or systems that can’t spare the resources to build and rebuild sensors. Alternatively, newer kernels support BPF features that take care of this for the developer, facilitating true Compile-Once, Run-Everywhere (CO-RE). Unfortunately, for a variety of legitimate reasons, customers aren’t always running newer kernels.

To tackle this problem, we’re building a system called the Linux Kernel Component Cloud Builder (LKCCB). LKCCB is an automated system that determines structure offsets for every kernel version and distribution we want to run our BPF probes on. These kernel offsets will then be dynamically loaded into the probes at runtime (using oxidebpf’s BPF hashmap interface). The probes will be able to check the loaded offsets and use them to navigate through kernel data structures appropriate for their host environment, returning exactly the information we’re looking for.

Think of it as a GPS for the Linux kernel. Our probes will be able to rely on it to find their way, without needing to memorize the lay of the land (i.e., compile on the host). Look out for its open source release in 2022!

What kind of results should I expect?

More system throughput

We benchmarked our eBPF probes in redcanary-ebpf-sensor against auditd by loading them with oxidebpf and comparing execl per second throughput using byte-unixbench. The system tested on was a set of four core virtual machines with 2GB of RAM each, running on a 3950X with 64GB of RAM. The baseline VM had a throughput of 19421.4 execl/s. With auditd set to trace execve and execveat events, we measured a throughput of 14187.4 execl/s. The equivalent set of eBPF probes from our sensor ran with a throughput of 16273.1 execl/s. That’s an approximate 15 percent increase in total system throughput, just for exec tracing. If we include the full auditd configuration required for our Linux sensor, the system throughput drops to 11989 execl/s. The equivalent set of eBPF probes from our sensor gets us a throughput of 14254 execl/s, an approximate 19 percent increase in throughput.

Collect information directly from the kernel

On some Linux kernel versions, we’ve experienced AuditD reporting incorrect inode numbers for containerized (i.e., namespaced) processes. AuditD notoriously struggles with containers, likely due to the subsystem predating the popularization of container technology. This requires a userspace workaround in which we query procfs for the information we miss. When AuditD is auditing process forks (i.e., clone, clone3, fork, vfork) it returns the child PID as-is from the system call’s return codes. The PID returned is in the PID namespace of the child, and not the root PID namespace. This makes it very difficult to use AuditD to keep track of process lineage in containerized environments. With eBPF, however, we can instrument a point in the kernel that’s on the return path from a process fork to the child process, and inspect the child process’s current task_struct to get the true root namespace PID.

By switching to BPF, we can collect inode information directly from the kernel. If there are kernel version-specific bugs, we can mitigate them by modifying or creating a new probe. The checks can happen in kernel space, avoiding the relatively slow and expensive check against procfs, as well as the inherent race conditions stemming from gathering data in multiple locations asynchronously.

How do I get started?

You can find all of our eBPF for security tools on GitHub:

As always, we welcome and encourage you to contribute!

 

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