Yearly Archives: 2024

Advisories

USN-7094-1: QEMU vulnerabilities

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It was discovered that QEMU incorrectly handled memory during certain VNC
operations. A remote attacker could possibly use this issue to cause QEMU
to consume resources, resulting in a denial of service. This issue only
affected Ubuntu 14.04 LTS. (CVE-2019-20382)

It was discovered that QEMU incorrectly handled certain memory copy
operations when loading ROM contents. If a user were tricked into running
an untrusted kernel image, a remote attacker could possibly use this issue
to run arbitrary code. This issue only affected Ubuntu 14.04 LTS.
(CVE-2020-13765)

Aviv Sasson discovered that QEMU incorrectly handled Slirp networking. A
remote attacker could use this issue to cause QEMU to crash, resulting in a
denial of service, or possibly execute arbitrary code. This issue only
affected Ubuntu 14.04 LTS. (CVE-2020-1983)

It was discovered that the SLiRP networking implementation of the QEMU
emulator did not properly manage memory under certain circumstances. An
attacker could use this to cause a heap-based buffer overflow or other out-
of-bounds access, which can lead to a denial of service (application crash)
or potential execute arbitrary code. This issue only affected
Ubuntu 14.04 LTS. (CVE-2020-7039)

It was discovered that the SLiRP networking implementation of the QEMU
emulator misuses snprintf return values. An attacker could use this to
cause a denial of service (application crash) or potentially execute
arbitrary code. This issue only affected Ubuntu 14.04 LTS. (CVE-2020-8608)

It was discovered that QEMU SLiRP networking incorrectly handled certain
udp packets. An attacker inside a guest could possibly use this issue to
leak sensitive information from the host. This issue only affected
Ubuntu 14.04 LTS and Ubuntu 16.04 LTS. (CVE-2021-3592, CVE-2021-3594)

It was discovered that QEMU had a DMA reentrancy issue, leading to a
use-after-free vulnerability. An attacker could possibly use this issue
to cause a denial of service. This issue only affected Ubuntu 18.04 LTS,
Ubuntu 20.04 LTS and Ubuntu 22.04 LTS. (CVE-2023-3019)

It was discovered that QEMU had a flaw in Virtio PCI Bindings, leading
to a triggerable crash via vhost_net_stop. An attacker inside a guest
could possibly use this issue to cause a denial of service. This issue
only affected Ubuntu 24.04 LTS and Ubuntu 24.10. (CVE-2024-4693)

It was discovered that QEMU incorrectly handled memory in virtio-sound,
leading to a heap-based buffer overflow. An attacker could possibly use
this issue to cause a denial of service or execute arbitrary code. This
issue only affected Ubuntu 24.04 LTS and Ubuntu 24.10. (CVE-2024-7730)

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Advisories

A Vulnerability in Android OS Could Allow for Remote Code Execution

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A vulnerability has been discovered in Android OS that could allow for remote code execution. Android is an operating system developed by Google for mobile devices, including, but not limited to, smartphones, tablets, and watches. Successful exploitation of this vulnerability could allow for remote code execution in the context of the logged-on user. Depending on the privileges associated with the user, an attacker could then install programs; view, change, or delete data; or create new accounts with full user rights. Users whose accounts are configured to have fewer user rights on the system could be less impacted than those who operate with administrative user rights.

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News

Prompt Injection Defenses Against LLM Cyberattacks

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Interesting research: “Hacking Back the AI-Hacker: Prompt Injection as a Defense Against LLM-driven Cyberattacks“:

Large language models (LLMs) are increasingly being harnessed to automate cyberattacks, making sophisticated exploits more accessible and scalable. In response, we propose a new defense strategy tailored to counter LLM-driven cyberattacks. We introduce Mantis, a defensive framework that exploits LLMs’ susceptibility to adversarial inputs to undermine malicious operations. Upon detecting an automated cyberattack, Mantis plants carefully crafted inputs into system responses, leading the attacker’s LLM to disrupt their own operations (passive defense) or even compromise the attacker’s machine (active defense). By deploying purposefully vulnerable decoy services to attract the attacker and using dynamic prompt injections for the attacker’s LLM, Mantis can autonomously hack back the attacker. In our experiments, Mantis consistently achieved over 95% effectiveness against automated LLM-driven attacks. To foster further research and collaboration, Mantis is available as an open-source tool: this https URL.

This isn’t the solution, of course. But this sort of thing could be part of a solution.

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Advisories

USN-6882-2: Cinder regression

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USN-6882-1 fixed vulnerabilities in Cinder. The update caused a regression
in certain environments due to incorrect privilege handling. This update
fixes the problem.

We apologize for the inconvenience.

Original advisory details:

Martin Kaesberger discovered that Cinder incorrectly handled QCOW2 image
processing. An authenticated user could use this issue to access arbitrary
files on the server, possibly exposing sensitive information.

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News

Subverting LLM Coders

Read Time:1 Minute, 5 Second

Really interesting research: “An LLM-Assisted Easy-to-Trigger Backdoor Attack on Code Completion Models: Injecting Disguised Vulnerabilities against Strong Detection“:

Abstract: Large Language Models (LLMs) have transformed code com-
pletion tasks, providing context-based suggestions to boost developer productivity in software engineering. As users often fine-tune these models for specific applications, poisoning and backdoor attacks can covertly alter the model outputs. To address this critical security challenge, we introduce CODEBREAKER, a pioneering LLM-assisted backdoor attack framework on code completion models. Unlike recent attacks that embed malicious payloads in detectable or irrelevant sections of the code (e.g., comments), CODEBREAKER leverages LLMs (e.g., GPT-4) for sophisticated payload transformation (without affecting functionalities), ensuring that both the poisoned data for fine-tuning and generated code can evade strong vulnerability detection. CODEBREAKER stands out with its comprehensive coverage of vulnerabilities, making it the first to provide such an extensive set for evaluation. Our extensive experimental evaluations and user studies underline the strong attack performance of CODEBREAKER across various settings, validating its superiority over existing approaches. By integrating malicious payloads directly into the source code with minimal transformation, CODEBREAKER challenges current security measures, underscoring the critical need for more robust defenses for code completion.

Clever attack, and yet another illustration of why trusted AI is essential.

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