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VU#980487: Local privilege escalation in Linux Kernel (Dirty Frag)

VU#980487: Local privilege escalation in Linux Kernel (Dirty Frag)

Overview
A privilege escalation vulnerability, nicknamed “Dirty Frag,” has been discovered in the Linux kernel versions 4.10 and later. This vulnerability is a result of chaining together two previously discovered vulnerabilities, xfrm-ESP Page-Cache Write CVE-2026-43284 and the RxRPC Page-Cache Write CVE-2026-43500. This vulnerability was publicly disclosed on May 07, 2026.
Description
Dirty Frag is a Linux kernel vulnerability affecting the IPv4/IPv6 fragmentation and reassembly subsystem. The issue stems from improper handling of overlapping or malformed fragment offsets during the reassembly process. An attacker capable of sending crafted network packets to a vulnerable host can exploit the flaw to trigger memory corruption conditions.
The publicly documented proof of concept demonstrates that fragmentation logic can be manipulated such that the kernel processes inconsistent fragment states, enabling a controlled write out-of-bounds scenario. When successfully exploited, this can result in local or remote denial of service (kernel panic) and, depending on configuration and kernel build options, may create a primitive for more advanced memory manipulation.
The vulnerability arises from insufficient validation of fragment metadata during reassembly, specifically around:

Incorrect or incomplete enforcement of fragment boundary checks
Acceptance of overlapping fragments in unsafe sequences
Inadequate cleanup when transitions occur between valid and invalid fragment states

The fragment queue logic in affected kernels does not fully verify that fragment offsets, sizes, and overlap conditions remain consistent throughout reassembly. This allows malformed sequences to be processed without proper rejection.
Impact
The primary security concern is potential privilege escalation, similar in nature to the previously disclosed VU#260001 (“Copy Fail”) vulnerability.
Depending on system configuration, kernel hardening features, and network exposure, successful exploitation may result in:

Local or remote denial of service through kernel panic
Memory corruption within the Linux networking stack
Privilege escalation
Container escape in certain containerized environments
Additional exploit primitives when chained with other vulnerabilities

Solution
Update Linux distribution
Update your distribution’s kernel package as soon as vendor patches become available. Most major Linux distributions are expected to release fixes through their standard update channels.
Workarounds (if patching is not immediately possible):
1) Disable at-risk modules (if loaded and loadable):
Use the following command to remove the modules in which the vulnerabilities occur and clear the page cache.
sh -c “printf ‘install esp4 /bin/falseninstall esp6 /bin/falseninstall rxrpc /bin/falsen’ > /etc/modprobe.d/dirtyfrag.conf; rmmod esp4 esp6 rxrpc 2>/dev/null; echo 3 > /proc/sys/vm/drop_caches; true”
Note: you can verify if a module is currently being used using lsmod and the Used field or reviewing refcnt data in /sys/module/<module_name>/refcnt for e.g., cat /sys/module/esp4/refcnt
2) If affected modules esp4, esp6, rxrpc are compiled into the kernel (not a dynamic module), the following parameter can be added to grub, systemd-boot, or grubby, depending on your boot configuration:
initcall_blacklist=esp4,esp6,rxrpc
This prevents the module from initializing at boot time. A system reboot is required for this change to take effect.
Mitigation for Containers
For containerized environments, where this vulnerability may be leveraged for container escape, consider applying one or more of the following mitigations:

Secure computing (seccomp) filtering: Restrict or deny system calls that create sockets using the AF_ALG address family (protocol 38) and AF_RXRPC (protocol 33) .
AppArmor policies: Use AppArmor to block creation of AF_ALG sockets and AF_RXRPC via the network alg rule.
eBPF-based enforcement: Deploy BPF-based controls to deny socket creation with address family AF_ALG (38) and AF_RXRPC (33).

Acknowledgements
This vulnerability was disclosed by Hyunwoo Kim. This document was written by Bob Kemerer.

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VU#777338: SGLang contains two remote code execution and one path traversal vulnerability

VU#777338: SGLang contains two remote code execution and one path traversal vulnerability

Overview
Three vulnerabilities have been discovered in the SGLang project, two enabling remote code execution (RCE), and one regarding a path traversal vulnerability. In order for an attacker to exploit these vulnerabilities, the multimodal generation mode must be enabled, and an attacker must have network access to the SGLang service. No patch is available at this time, and no response was obtained from the project maintainers during coordination.
Description
SGLang is an open-source framework for serving large language models (LLMs) and multimodal AI models, supporting models such as Qwen, DeepSeek, Mistral, and Skywork, and is compatible with OpenAI APIs. Three vulnerabilities have been discovered within the tool and are tracked as follows:
CVE-2026-7301
The multimodal generation runtime scheduler’s ROUTER socket contains a sink that calls pickle.loads() on incoming messages, enabling RCE when exposed to the internet.
This vulnerability is distinct from CVE-2026-3060 and CVE-2026-3059, which would be open to the Internet via the ZMQ broker, which automatically binded to all network interfaces without user awareness. CVE-2026-7301 is exposed to the internet by default through the scheduler host, which binds to 0.0.0.0 by default.
CVE-2026-7302
The multimodal generation runtime is vulnerable to an unauthenticated path traversal vulnerability, allowing an attacker to write arbitrary files anywhere the server process has write access, by including ../ sequences in the upload filename when sent to specific endpoints.
CVE-2026-7304
The multimodal generation runtime is vulnerable to unauthenticated remote code execution when the –enable-custom-logit-processor option is enabled, as Python objects loaded via dill.loads() will be deserialized without validation.
Impact
If exploited, these vulnerabilities could allow an unauthenticated attacker to achieve remote code execution or arbitrary file writes on the host running SGLang. Deployments that expose the affected interface to untrusted networks are at the highest risk of exploitation.
Solution
Until a patch is available, affected users should consider the following mitigations:
Mitigation

Restrict access to the service interfaces and ensure they are not exposed to untrusted networks.
Implement network segmentation and access controls to prevent unauthorized interaction with the vulnerable endpoints.

Acknowledgements
Thanks to the reporter, Alon Shakevsky. This document was written by Christopher Cullen.

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AI Security and Privacy Concerns

Is Your Data Safe? The Privacy and Security Risks of Using AI You Need to Know

Privacy and security concerns around AI are not hypothetical. They are real, present, and often misunderstood. This article breaks down the key risks, explains how AI companies handle your data, and gives you practical steps to protect yourself.

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