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cve[email protected]CVE-2024-34359
HistoryMay 14, 2024 - 3:38 p.m.

CVE-2024-34359

2024-05-1415:38:45
CWE-76
web.nvd.nist.gov
33
llama-cpp-python
llama
.gguf
latency machine learning models
numa
lora settings
loading tokenizers
hardware settings
chat template
jinja2chatformatter
jinja2.environment
server side template injection
remote code execution

9.6 High

CVSS3

Attack Vector

NETWORK

Attack Complexity

LOW

Privileges Required

NONE

User Interaction

REQUIRED

Scope

CHANGED

Confidentiality Impact

HIGH

Integrity Impact

HIGH

Availability Impact

HIGH

CVSS:3.1/AV:N/AC:L/PR:N/UI:R/S:C/C:H/I:H/A:H

8.3 High

AI Score

Confidence

Low

6.8 Medium

CVSS2

Access Vector

NETWORK

Access Complexity

MEDIUM

Authentication

NONE

Confidentiality Impact

PARTIAL

Integrity Impact

PARTIAL

Availability Impact

PARTIAL

AV:N/AC:M/Au:N/C:P/I:P/A:P

0.0004 Low

EPSS

Percentile

8.7%

llama-cpp-python is the Python bindings for llama.cpp. llama-cpp-python depends on class Llama in llama.py to load .gguf llama.cpp or Latency Machine Learning Models. The __init__ constructor built in the Llama takes several parameters to configure the loading and running of the model. Other than NUMA, LoRa settings, loading tokenizers, and hardware settings, __init__ also loads the chat template from targeted .gguf 's Metadata and furtherly parses it to llama_chat_format.Jinja2ChatFormatter.to_chat_handler() to construct the self.chat_handler for this model. Nevertheless, Jinja2ChatFormatter parse the chat template within the Metadate with sandbox-less jinja2.Environment, which is furthermore rendered in __call__ to construct the prompt of interaction. This allows jinja2 Server Side Template Injection which leads to remote code execution by a carefully constructed payload.

9.6 High

CVSS3

Attack Vector

NETWORK

Attack Complexity

LOW

Privileges Required

NONE

User Interaction

REQUIRED

Scope

CHANGED

Confidentiality Impact

HIGH

Integrity Impact

HIGH

Availability Impact

HIGH

CVSS:3.1/AV:N/AC:L/PR:N/UI:R/S:C/C:H/I:H/A:H

8.3 High

AI Score

Confidence

Low

6.8 Medium

CVSS2

Access Vector

NETWORK

Access Complexity

MEDIUM

Authentication

NONE

Confidentiality Impact

PARTIAL

Integrity Impact

PARTIAL

Availability Impact

PARTIAL

AV:N/AC:M/Au:N/C:P/I:P/A:P

0.0004 Low

EPSS

Percentile

8.7%

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