Vulnerabilities > CVE-2024-41009 - Allocation of Resources Without Limits or Throttling vulnerability in Linux Kernel
Summary
In the Linux kernel, the following vulnerability has been resolved: bpf: Fix overrunning reservations in ringbuf The BPF ring buffer internally is implemented as a power-of-2 sized circular buffer, with two logical and ever-increasing counters: consumer_pos is the consumer counter to show which logical position the consumer consumed the data, and producer_pos which is the producer counter denoting the amount of data reserved by all producers. Each time a record is reserved, the producer that "owns" the record will successfully advance producer counter. In user space each time a record is read, the consumer of the data advanced the consumer counter once it finished processing. Both counters are stored in separate pages so that from user space, the producer counter is read-only and the consumer counter is read-write. One aspect that simplifies and thus speeds up the implementation of both producers and consumers is how the data area is mapped twice contiguously back-to-back in the virtual memory, allowing to not take any special measures for samples that have to wrap around at the end of the circular buffer data area, because the next page after the last data page would be first data page again, and thus the sample will still appear completely contiguous in virtual memory. Each record has a struct bpf_ringbuf_hdr { u32 len; u32 pg_off; } header for book-keeping the length and offset, and is inaccessible to the BPF program. Helpers like bpf_ringbuf_reserve() return `(void *)hdr + BPF_RINGBUF_HDR_SZ` for the BPF program to use. Bing-Jhong and Muhammad reported that it is however possible to make a second allocated memory chunk overlapping with the first chunk and as a result, the BPF program is now able to edit first chunk's header. For example, consider the creation of a BPF_MAP_TYPE_RINGBUF map with size of 0x4000. Next, the consumer_pos is modified to 0x3000 /before/ a call to bpf_ringbuf_reserve() is made. This will allocate a chunk A, which is in [0x0,0x3008], and the BPF program is able to edit [0x8,0x3008]. Now, lets allocate a chunk B with size 0x3000. This will succeed because consumer_pos was edited ahead of time to pass the `new_prod_pos - cons_pos > rb->mask` check. Chunk B will be in range [0x3008,0x6010], and the BPF program is able to edit [0x3010,0x6010]. Due to the ring buffer memory layout mentioned earlier, the ranges [0x0,0x4000] and [0x4000,0x8000] point to the same data pages. This means that chunk B at [0x4000,0x4008] is chunk A's header. bpf_ringbuf_submit() / bpf_ringbuf_discard() use the header's pg_off to then locate the bpf_ringbuf itself via bpf_ringbuf_restore_from_rec(). Once chunk B modified chunk A's header, then bpf_ringbuf_commit() refers to the wrong page and could cause a crash. Fix it by calculating the oldest pending_pos and check whether the range from the oldest outstanding record to the newest would span beyond the ring buffer size. If that is the case, then reject the request. We've tested with the ring buffer benchmark in BPF selftests (./benchs/run_bench_ringbufs.sh) before/after the fix and while it seems a bit slower on some benchmarks, it is still not significantly enough to matter.
Vulnerable Configurations
Common Weakness Enumeration (CWE)
Common Attack Pattern Enumeration and Classification (CAPEC)
- Locate and Exploit Test APIs An attacker exploits a sample, demonstration, or test API that is insecure by default and should not be resident on production systems. Some applications include APIs that are intended to allow an administrator to test and refine their domain. These APIs should usually be disabled once a system enters a production environment. Testing APIs may expose a great deal of diagnostic information intended to aid an administrator, but which can also be used by an attacker to further refine their attack. Moreover, testing APIs may not have adequate security controls or may not have undergone rigorous testing since they were not intended for use in production environments. As such, they may have many flaws and vulnerabilities that would allow an attacker to severely disrupt a target.
- Flooding An attacker consumes the resources of a target by rapidly engaging in a large number of interactions with the target. This type of attack generally exposes a weakness in rate limiting or flow control in management of interactions. Since each request consumes some of the target's resources, if a sufficiently large number of requests must be processed at the same time then the target's resources can be exhausted. The degree to which the attack is successful depends upon the volume of requests in relation to the amount of the resource the target has access to, and other mitigating circumstances such as the target's ability to shift load or acquired additional resources to deal with the depletion. The more protected the resource and the greater the quantity of it that must be consumed, the more resources the attacker may need to have at their disposal. A typical TCP/IP flooding attack is a Distributed Denial-of-Service attack where many machines simultaneously make a large number of requests to a target. Against a target with strong defenses and a large pool of resources, many tens of thousands of attacking machines may be required. When successful this attack prevents legitimate users from accessing the service and can cause the target to crash. This attack differs from resource depletion through leaks or allocations in that the latter attacks do not rely on the volume of requests made to the target but instead focus on manipulation of the target's operations. The key factor in a flooding attack is the number of requests the attacker can make in a given period of time. The greater this number, the more likely an attack is to succeed against a given target.
- Excessive Allocation An attacker causes the target to allocate excessive resources to servicing the attackers' request, thereby reducing the resources available for legitimate services and degrading or denying services. Usually, this attack focuses on memory allocation, but any finite resource on the target could be the attacked, including bandwidth, processing cycles, or other resources. This attack does not attempt to force this allocation through a large number of requests (that would be Resource Depletion through Flooding) but instead uses one or a small number of requests that are carefully formatted to force the target to allocate excessive resources to service this request(s). Often this attack takes advantage of a bug in the target to cause the target to allocate resources vastly beyond what would be needed for a normal request. For example, using an Integer Attack, the attacker could cause a variable that controls allocation for a request to hold an excessively large value. Excessive allocation of resources can render a service degraded or unavailable to legitimate users and can even lead to crashing of the target.
- XML Ping of the Death An attacker initiates a resource depletion attack where a large number of small XML messages are delivered at a sufficiently rapid rate to cause a denial of service or crash of the target. Transactions such as repetitive SOAP transactions can deplete resources faster than a simple flooding attack because of the additional resources used by the SOAP protocol and the resources necessary to process SOAP messages. The transactions used are immaterial as long as they cause resource utilization on the target. In other words, this is a normal flooding attack augmented by using messages that will require extra processing on the target.
- XML Entity Expansion An attacker submits an XML document to a target application where the XML document uses nested entity expansion to produce an excessively large output XML. XML allows the definition of macro-like structures that can be used to simplify the creation of complex structures. However, this capability can be abused to create excessive demands on a processor's CPU and memory. A small number of nested expansions can result in an exponential growth in demands on memory.