Preface: Apache Kafka was originally developed by Linkedin to solve data pipeline problems, and was later donated to the Apache Foundation as an open source project.

Background: Avro facilitates the exchange of big data between programs written in any language. With the serialization service, programs can efficiently serialize data into files or into messages. The data storage is compact and efficient. Avro stores both the data definition and the data together in one message or file. SANnav uses Kafka technology to push data out to a consumer that is configured to receive it. Clients receive a secure binary stream of telemetry data pushed periodically.

The Schema Registry is an external process that runs on a server outside of your Kafka cluster. It is essentially a database for the schemas used in your Kafka environment and handles the distribution and synchronization of schemas to the producer and consumer by storing a copy of the schema in its local cache.

Vulnerability details: A vulnerability in Brocade SANnav exposes Kafka in the wan interface. The vulnerability could allow an unauthenticated attacker to perform various attacks, including DOS against the Brocade SANnav.

Remark: There is no official explanation as to why this user “sat nav” was created in the Linux operating system. But now it no longer exists. But the possible reason for this vulnerability is that the service user ID and password are hard-coded!

Official announcement: Please refer to the link for details – https://nvd.nist.gov/vuln/detail/CVE-2024-4173

https://support.broadcom.com/web/ecx/support-content-notification/-/external/content/SecurityAdvisories/0/23285

Preface: Modern Linux systems require large amount of graphics memory to store frame buffers, textures, vertices and other graphics-related data. If a design flaw like CVE-2024-26922 cannot be fixed. It may trigger privilege escalation.

Background: So far, GPU-attached bandwidth optimized (BO) memory has been allocated and managed primarily as the result of explicit, programmer-directed function calls. The DRM core includes two memory managers, namely Translation Table Manager (TTM) and Graphics Execution Manager (GEM).

Vulnerability details: In the Linux kernel, the following vulnerability has been resolved: drm/amdgpu: validate the parameters of bo mapping operations more clearly Verify the parameters of amdgpu_vm_bo_(map/replace_map/clearing_mappings) in one common place.

Official announcement: Please refer to the link for details – https://nvd.nist.gov/vuln/detail/CVE-2024-26922

Preface: Open vSwitch is a production quality, multilayer virtual switch licensed under the open source Apache 2.0 license.  It is designed to enable massive network automation through programmatic extension, while still supporting standard management interfaces and protocols (e.g. NetFlow, sFlow, IPFIX, RSPAN, CLI, LACP, 802.1ag).  In addition, it is designed to support distribution across multiple physical servers similar to VMware’s vNetwork distributed vswitch or Cisco’s Nexus 1000V.

Background: The buffers used by the kernel to manage network packets are referred to as sk_buffs in Linux. The buffers are always allocated as at least two separate components: a fixed size header of type struct sk_buff; and a variable length area large enough to hold all or part of the data of a single packet.

Vulnerability details: The vulnerability details involve 4 key files. The explanation may refer to CVE details. Please refer to the link for details –

https://nvd.nist.gov/vuln/detail/CVE-2024-26921

Security Focus: A relevant old patch about the issue was : 8282f27449bf (“inet: frag: Always orphan skbs inside ip_defrag()”) [..] net/ipv4/ip_output[.]c depends on skb->sk being set, and probably to an inet socket, not an arbitrary one. If we orphan the packet in ipvlan, then downstream things like FQ packet scheduler will not work properly. We need to change ip_defrag() to only use skb_orphan() when really needed, ie whenever frag_list is going to be used.

TX: skb->sk might have been passed as argument to dst->output and must remain valid until tx completes. Move sk to reassembled skb and fix up wmem accounting.

Preface: Using the C++ new operator, we can allocate memory at the runtime. The new operator in C++ is used for the dynamic memory allocation; It is used to allocate the memory at runtime on heap memory.

Background: PyTorch is a deep learning framework open sourced by Facebook in early 2017. It is built on Torch and is advertised as Python First. It is tailor-made for the Python language. PyTorch is unique in that it fully supports GPUs and uses reverse-mode automatic differentiation technology, so the computational graph can be modified dynamically. This makes it a popular choice for rapid experimentation and prototyping.

Vulnerability details: PyTorch before v2.2.0 was discovered to contain a heap buffer overflow vulnerability in the component /runtime/vararg_functions.cpp. This vulnerability allows attackers to cause a Denial of Service (DoS) via a crafted input.

Official announcement: Please refer to the link for details –

https://nvd.nist.gov/vuln/detail/CVE-2024-31580

Preface: Training is the most important step in machine learning. In training, you pass the prepared data to your machine learning model to find patterns and make predictions. It results in the model learning from the data so that it can accomplish the task set.

Background: What is Apache Zeppelin? Apache Zeppelin is an open-source, web-based notebook that enables data visualization, data exploration, and collaborative data analytics. Apache Zeppelin interpreter supports several language backends, including Apache Spark, Python, R, JDBC, Apache Flink, Markdown, and Shell.

By integrating submarine in zeppelin, we use zeppelin’s data discovery, data analysis and data visualization and collaboration capabilities to visualize the results of algorithm development and parameter adjustment during machine learning model training.

Vulnerability details: Improper Control of Generation of Code (‘Code Injection’) vulnerability in Apache Zeppelin. The attackers can use Shell interpreter as a code generation gateway, and execute the generated code as a normal way. This issue affects Apache Zeppelin: from 0.10.1 before 0.11.1. Users are recommended to upgrade to version 0.11.1, which doesn’t have Shell interpreter by default.

Official announcement: Please refer to the link for details – https://nvd.nist.gov/vuln/detail/CVE-2024-31861

Preface: A CUDA binary (also referred to as cubin) file is an ELF-formatted file which consists of CUDA executable code sections as well as other sections containing symbols, relocators, debug info, etc. By default, the CUDA compiler driver nvcc embeds cubin files into the host executable file.

Background: To dump cuda elf sections in human readable format from a cubin file, use the following command: cuobjdump -elf <cubin file>

nvdisasm extracts information from standalone cubin files and presents them in human readable format. The output of nvdisasm includes CUDA assembly code for each kernel, listing of ELF data sections and other CUDA specific sections.

–base-address <value>

–base

Desc: Specify the logical base address of the image to disassemble. This option is only valid when disassembling a raw instruction binary (see option –binary), and is ignored when disassembling an Elf file. Default value: 0.

Vulnerability details: CVE‑2024‑0072 and CVE-2024-0076: NVIDIA CUDA toolkit for all platforms contains a vulnerability in cuobjdump and nvdisasm where an attacker may cause a crash by tricking a user into reading a malformed ELF file. A successful exploit of this vulnerability may lead to a partial denial of service.

Official announcement: Please refer to the link for details – https://nvidia.custhelp.com/app/answers/detail/a_id/5517

Preface: Imagine that you are training your application to differentiate between two types of cars (Ferrari and Porsche). You show the app numerous images of both cars, from appearance to features to engine design. Over time, apps begin to recognize the unique features that distinguish each other. At this point, the application can tell the difference between the two without help, which is basically what your Machine Learning model is used for. We call this phase training.

Background: LocalAI is a drop-in replacement REST API compatible with OpenAI API specifications for local inferencing. It allows to run models locally or on-prem with consumer grade hardware (No need for expensive cloud services or GPUs), supporting multiple models families compatible with the ggml format.

Vulnerability details: The web server lacked CSRF tokens allowing an attacker to host malicious JavaScript on a host that when visited by a LocalAI user, could allow the attacker to fill disk space to deny service or abuse credits.

Ref: Why missing CSRF looks common? This can be caused by ad- or script-blocking plugins or extensions and the browser itself if it’s not allowed to set cookies.

Official announcement: Please refer to the link for details – https://nvd.nist.gov/vuln/detail/CVE-2024-3135

Preface: Unlike OpenAI’s ChatGPT, Chat with RTX doesn’t remember the context of prompts. Asking Chat with RTX to give examples of fishes in one prompt and then asking for a description of “the fishes” in the next prompt will result in a blank – users will need to spell out everything explicitly.

Background: Chat with RTX defaults to AI startup Mistral’s open-source model but supports other text-based models, including Meta’s Llama 2, which is also open-source.

Chat with RTX is a demo app that lets you personalize a GPT large language model (LLM) connected to your own content—docs, notes, videos, or other data. Leveraging retrieval-augmented generation (RAG), TensorRT-LLM, and RTX acceleration, you can query a custom chatbot to quickly get contextually relevant answers. And because it all runs locally on your Windows RTX PC or workstation.

Vulnerability details: NVIDIA ChatRTX for Windows contains a vulnerability in the UI, where an attacker can cause improper privilege management by sending open file requests to the application. A successful exploit of this vulnerability might lead to local escalation of privileges, information disclosure, and data tampering.

Official announcement: Please refer to the link for details – https://nvidia.custhelp.com/app/answers/detail/a_id/5532