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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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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Subverting LLM Coders

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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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USN-7088-4: Linux kernel vulnerabilities

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Ziming Zhang discovered that the VMware Virtual GPU DRM driver in the
Linux kernel contained an integer overflow vulnerability. A local
attacker could use this to cause a denial of service (system crash).
(CVE-2022-36402)

Several security issues were discovered in the Linux kernel.
An attacker could possibly use these to compromise the system.
This update corrects flaws in the following subsystems:
– ARM64 architecture;
– PowerPC architecture;
– User-Mode Linux (UML);
– x86 architecture;
– Block layer subsystem;
– Cryptographic API;
– Android drivers;
– Serial ATA and Parallel ATA drivers;
– ATM drivers;
– Drivers core;
– CPU frequency scaling framework;
– Device frequency scaling framework;
– GPU drivers;
– HID subsystem;
– Hardware monitoring drivers;
– InfiniBand drivers;
– Input Device core drivers;
– Input Device (Miscellaneous) drivers;
– IOMMU subsystem;
– IRQ chip drivers;
– ISDN/mISDN subsystem;
– LED subsystem;
– Multiple devices driver;
– Media drivers;
– EEPROM drivers;
– VMware VMCI Driver;
– MMC subsystem;
– Network drivers;
– Near Field Communication (NFC) drivers;
– NVME drivers;
– Device tree and open firmware driver;
– Parport drivers;
– PCI subsystem;
– Pin controllers subsystem;
– Remote Processor subsystem;
– S/390 drivers;
– SCSI drivers;
– QCOM SoC drivers;
– Direct Digital Synthesis drivers;
– TTY drivers;
– Userspace I/O drivers;
– DesignWare USB3 driver;
– USB Gadget drivers;
– USB Serial drivers;
– BTRFS file system;
– File systems infrastructure;
– Ext4 file system;
– F2FS file system;
– JFS file system;
– NILFS2 file system;
– BPF subsystem;
– Core kernel;
– DMA mapping infrastructure;
– Tracing infrastructure;
– Radix Tree data structure library;
– Kernel userspace event delivery library;
– Objagg library;
– Memory management;
– Amateur Radio drivers;
– Bluetooth subsystem;
– CAN network layer;
– Networking core;
– Ethtool driver;
– IPv4 networking;
– IPv6 networking;
– IUCV driver;
– KCM (Kernel Connection Multiplexor) sockets driver;
– MAC80211 subsystem;
– Netfilter;
– Network traffic control;
– SCTP protocol;
– Sun RPC protocol;
– TIPC protocol;
– TLS protocol;
– Wireless networking;
– AppArmor security module;
– Simplified Mandatory Access Control Kernel framework;
– SoC audio core drivers;
– USB sound devices;
(CVE-2024-35848, CVE-2024-43853, CVE-2024-41017, CVE-2024-26607,
CVE-2024-43839, CVE-2024-41072, CVE-2024-46815, CVE-2023-52614,
CVE-2024-46798, CVE-2024-46676, CVE-2024-43914, CVE-2024-43841,
CVE-2024-41012, CVE-2024-27051, CVE-2024-46738, CVE-2024-47663,
CVE-2024-46723, CVE-2024-46740, CVE-2024-42287, CVE-2024-46750,
CVE-2024-43894, CVE-2023-52531, CVE-2024-47668, CVE-2024-47669,
CVE-2024-46685, CVE-2024-41011, CVE-2024-41064, CVE-2024-42305,
CVE-2024-41073, CVE-2024-46829, CVE-2024-43860, CVE-2024-46679,
CVE-2024-44999, CVE-2024-46817, CVE-2024-26800, CVE-2024-46689,
CVE-2024-43908, CVE-2024-46739, CVE-2024-43893, CVE-2024-46828,
CVE-2024-46777, CVE-2024-46721, CVE-2024-36484, CVE-2024-46822,
CVE-2024-46840, CVE-2024-43880, CVE-2024-46781, CVE-2024-46673,
CVE-2024-26669, CVE-2024-41098, CVE-2024-46737, CVE-2024-43871,
CVE-2024-42281, CVE-2024-42301, CVE-2024-44995, CVE-2024-43879,
CVE-2024-26668, CVE-2024-44965, CVE-2024-41068, CVE-2024-41059,
CVE-2024-42229, CVE-2024-44987, CVE-2024-46745, CVE-2024-26891,
CVE-2024-46719, CVE-2024-42292, CVE-2024-44952, CVE-2024-46756,
CVE-2024-45028, CVE-2024-42283, CVE-2024-45025, CVE-2024-46743,
CVE-2024-43867, CVE-2024-46771, CVE-2024-41081, CVE-2024-42244,
CVE-2024-42284, CVE-2024-43858, CVE-2024-44998, CVE-2024-46758,
CVE-2024-46800, CVE-2024-45003, CVE-2024-44935, CVE-2024-38611,
CVE-2024-46844, CVE-2024-44954, CVE-2024-42313, CVE-2024-46783,
CVE-2024-42311, CVE-2024-46761, CVE-2024-41022, CVE-2024-43829,
CVE-2024-43835, CVE-2024-43846, CVE-2024-46755, CVE-2024-47667,
CVE-2024-42259, CVE-2024-41090, CVE-2024-42310, CVE-2024-42265,
CVE-2024-42295, CVE-2024-46818, CVE-2024-46780, CVE-2024-44948,
CVE-2024-44960, CVE-2024-44988, CVE-2024-46757, CVE-2024-45021,
CVE-2024-46747, CVE-2024-43854, CVE-2024-42304, CVE-2021-47212,
CVE-2024-42309, CVE-2024-44946, CVE-2024-46744, CVE-2024-42285,
CVE-2024-46782, CVE-2024-43856, CVE-2024-41091, CVE-2024-42131,
CVE-2024-43830, CVE-2024-42290, CVE-2024-45008, CVE-2024-42276,
CVE-2024-47659, CVE-2024-40929, CVE-2024-46714, CVE-2023-52918,
CVE-2024-44947, CVE-2024-42289, CVE-2024-42246, CVE-2024-41071,
CVE-2024-43883, CVE-2024-46722, CVE-2024-38602, CVE-2024-43882,
CVE-2024-42280, CVE-2024-46759, CVE-2024-42271, CVE-2024-44969,
CVE-2024-44944, CVE-2024-46675, CVE-2024-41020, CVE-2024-41042,
CVE-2024-42306, CVE-2024-46677, CVE-2024-42288, CVE-2024-41070,
CVE-2024-45026, CVE-2024-41065, CVE-2024-26885, CVE-2024-42286,
CVE-2024-41063, CVE-2024-43884, CVE-2024-42297, CVE-2024-43890,
CVE-2024-43861, CVE-2024-45006, CVE-2024-26640, CVE-2024-26641,
CVE-2024-41015)

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