All tracked items across vulnerabilities, news, research, incidents, and regulatory updates.
Running pip download (a Python command that downloads packages without installing them) can execute malicious code on your computer due to a design flaw, even though many people assume only pip install poses a security risk. This vulnerability allows attackers to run arbitrary code (commands of their choice) simply by downloading a compromised package.
Streamlit, a Python framework for building data applications, has a directory traversal vulnerability (a type of attack where an attacker uses specially crafted file paths to access files they shouldn't be able to reach) in versions before 1.11.1. An attacker could trick the Streamlit server into reading and returning sensitive files from the server's file system, such as logs or other confidential information.
Honeywell Experion PKS Safety Manager controllers have a vulnerability where control logic downloaded to the device is not cryptographically authenticated (meaning the system doesn't verify the logic came from a trusted source). This allows an attacker who can communicate with the controller to run arbitrary machine code on it, potentially taking control of safety systems similar to how the TRITON malware (a real-world attack on industrial control systems) operated.
Oracle Coherence, a data management product in Oracle Fusion Middleware, has a vulnerability (CVE-2022-21570) that allows attackers without authentication to crash the system through network protocols called T3 and IIOP. This affects versions 3.7.1.0, 12.2.1.3.0, 12.2.1.4.0, and 14.1.1.0.0, with a severity rating of 7.5 out of 10 (CVSS score, a standard measure of how serious a vulnerability is).
This post demonstrates how attackers can use eBPF (extended Berkeley Packet Filter, a technology that lets programs run safely inside the Linux kernel) and bpftrace (a tool for tracing system events) to intercept and steal PAM (Pluggable Authentication Modules, the system that handles login authentication) passwords when users log in. The technique works similarly to a backdoor PAM module but uses eBPF instead, highlighting emerging security risks from new kernel-level attack tools.
This post describes a post-exploitation attack where an attacker modifies PAM (Pluggable Authentication Modules, a Unix system for controlling login behavior) to secretly capture user passwords by inserting a malicious script into the authentication pipeline. The attacker creates a bash script that logs usernames, passwords, and IP addresses whenever someone logs in, making it difficult to detect because it operates within legitimate system authentication mechanisms.
CVE-2020-25459 is a vulnerability in WeBank FATE (Federated AI Technology Enabler, a system for training machine learning models across multiple parties) versions 0.1 through 1.4.2 that allows attackers to read sensitive information during the training process. The issue exists in a function called sync_tree in the hetero_decision_tree_guest.py file, which means attackers could access private data while the model is being trained.
CVE-2022-29540 is a cross-site scripting vulnerability (XSS, where attackers inject malicious code into web pages) in RESI Gemini-Net 4.2 that allows unauthenticated remote attackers to inject arbitrary web scripts or HTML through HTTP GET parameters without proper input validation. The vulnerability affects multiple application endpoints and has a CVSS score (severity rating on a 0-10 scale) of 4.0.
This article explains how to customize shell prompts (the command-line interface where users type commands) to automatically display date and time information, which helps security professionals document their activities during red team exercises (simulated attacks to test security). The article provides examples for three common shells: Bash, zsh on macOS, and PowerShell.
TensorFlow's `saved_model_cli` tool (a utility for working with saved machine learning models) had a code injection vulnerability in versions before 2.9.0, 2.8.1, 2.7.2, and 2.6.4, which could allow an attacker to open a reverse shell (a backdoor connection giving remote control of a system). The vulnerability existed because the tool used `eval` (a function that executes text as code) on user input for compatibility with older test cases, but since the tool requires manual operation, the practical risk was limited.
TensorFlow, an open source platform for machine learning, had a bug in two signal processing functions (`tf.compat.v1.signal.rfft2d` and `tf.compat.v1.signal.rfft3d`) where missing input validation (checking that data meets expected requirements before processing) could cause the software to crash under certain conditions. The bug was fixed in versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4.
TensorFlow, an open source machine learning platform, had a bug in versions before 2.9.0, 2.8.1, 2.7.2, and 2.6.4 where certain converted models would crash when loaded. The problem occurred because the code assumed that quantization (a technique to compress model size by reducing numerical precision) would always use scaling factors smaller than 1, but sometimes the scale was larger, causing the program to stop unexpectedly.
TensorFlow, an open source platform for machine learning, has a vulnerability in the `tf.histogram_fixed_width` function where it crashes if the input data contains NaN (Not a Number, a special floating point value representing undefined results). The crash happens because the code tries to convert NaN to an integer without checking for it first, and this bug only affects the CPU version of TensorFlow.
TensorFlow version 2.8.0 had a bug in the `TensorKey` hash function (a function that converts data into a fixed-size code for quick lookups), where it incorrectly used `AllocatedBytes()` (an estimate of memory used by a tensor, including referenced data like strings) to access the actual tensor data bytes. This caused crashes because `AllocatedBytes()` doesn't represent the real contiguous memory buffer, and certain data types like `tstring` contain pointers rather than actual values.
TensorFlow, an open source machine learning platform, had a bug in versions before 2.9.0, 2.8.1, 2.7.2, and 2.6.4 where assertion macros (special code blocks that check if conditions are true) incorrectly compared different data types, specifically `size_t` and `int` values (two different ways to store whole numbers). This type confusion could cause assertions to trigger incorrectly due to how the computer converts between these different number types.
TensorFlow, an open source platform for machine learning, has a vulnerability in the `tf.raw_ops.EditDistance` function where incomplete validation allows users to pass negative values that cause a segmentation fault (a program crash from accessing invalid memory). An attacker could exploit this by crafting input that produces negative array indices, allowing writes before the intended array location and potentially crashing the system.
CVE-2022-29206 is a bug in TensorFlow (an open source machine learning platform) where a specific function called `tf.raw_ops.SparseTensorDenseAdd` doesn't properly check its input arguments, causing a nullptr (a reference pointing to nothing) to be accessed during execution, which leads to undefined behavior. This vulnerability affects TensorFlow versions before 2.9.0, 2.8.1, 2.7.2, and 2.6.4.
TensorFlow (an open-source machine learning platform) has a bug in older versions where calling certain compatibility functions with unsupported data types causes the program to crash. When the code tries to process a missing function, it attempts to use a null pointer (a reference to nothing in memory), which causes a segmentation fault (a type of crash where the program accesses memory it shouldn't).
TensorFlow, an open source platform for machine learning, has a vulnerability in one of its operations called `tf.raw_ops.UnsortedSegmentJoin` where it doesn't properly check its inputs before using them. If someone provides a negative number where a positive one is expected, it causes the program to crash with an assertion failure, which is a type of denial of service attack (making software unavailable by crashing it).
Pickle files (Python's serialization format for saving objects) can be backdoored because they execute code through opcodes (instructions that control a virtual machine). Attackers can inject malicious commands into pickle files using tools like fickling, and when someone loads the file, the hidden code runs without interrupting the program's normal function. This is especially dangerous in shared environments like Google Colab, where an infected pickle file could give attackers access to a user's connected Google Drive.
Fix: The source mentions fickling, a tool by Trail of Bits that can both inject code into pickle files and check them for backdoors using two built-in safety features: '--check-safety' (which checks for malicious opcodes) and '--trace' (which shows the various opcodes). The source also recommends: "only ever open pickle files that you created or trust."
Embrace The RedFix: Upgrade to Streamlit version 1.11.1 or later. The source explicitly states, 'This issue has been resolved in version 1.11.1. Users are advised to upgrade.' No workarounds are available.
NVD/CVE DatabaseFix: The source explicitly mentions three mitigation strategies: (1) 'Test EDR to catch modifications in PAM configurations (also binary patching or entirely replacing/backdooring existing ones)', (2) 'Review the PAM modules and there configuration in your environments', and (3) 'Do a purple team exercise that focuses on PAM modules and related configuration files'.
Embrace The RedFix: Update TensorFlow to version 2.9.0, 2.8.1, 2.7.2, or 2.6.4 or later. The maintainers removed the `safe=False` argument, so all parsing is now done without calling `eval`.
NVD/CVE DatabaseFix: Update TensorFlow to one of the patched versions: 2.9.0, 2.8.1, 2.7.2, or 2.6.4.
NVD/CVE DatabaseFix: Update to TensorFlow versions 2.9.0, 2.8.1, 2.7.2, or 2.6.4, which contain a patch for this issue.
NVD/CVE DatabaseFix: Update to TensorFlow versions 2.9.0, 2.8.1, 2.7.2, or 2.6.4, which contain a patch for this issue.
NVD/CVE DatabaseFix: This issue is patched in TensorFlow versions 2.9.0 and 2.8.1.
NVD/CVE DatabaseFix: Update TensorFlow to version 2.9.0, 2.8.1, 2.7.2, or 2.6.4 or later, as these versions contain a patch for this issue.
NVD/CVE DatabaseFix: Update to TensorFlow versions 2.9.0, 2.8.1, 2.7.2, or 2.6.4, which contain a patch for this issue.
NVD/CVE DatabaseFix: Update TensorFlow to versions 2.9.0, 2.8.1, 2.7.2, or 2.6.4 or later, which contain a patch for this issue.
NVD/CVE DatabaseFix: Update to TensorFlow version 2.9.0, 2.8.1, 2.7.2, or 2.6.4 or later, which contain a patch for this issue.
NVD/CVE DatabaseFix: Update TensorFlow to version 2.9.0, 2.8.1, 2.7.2, or 2.6.4 or later, as these versions contain a patch for this issue.
NVD/CVE Database