CVE-2024-34359
Scores
EPSS
Percentile: 62.6%
CVSS
CVSS Score: 9.6/10
All CVSS Scores
Vector: CVSS:3.1/AV:N/AC:L/PR:N/UI:R/S:C/C:H/I:H/A:H
Vector Breakdown
CVSS (Common Vulnerability Scoring System) vector provides detailed metrics about vulnerability characteristics
CVSS
Attack Vector
Network (N)
Describes how the vulnerability is exploited
Attack Complexity
Low (L)
Describes the conditions beyond the attacker's control
Privileges Required
None (N)
Describes the level of privileges an attacker must possess
User Interaction
Required (R)
Captures the requirement for a human user participation
Scope
Changed (C)
Determines if a successful attack impacts components beyond the vulnerable component
Confidentiality Impact
High (H)
Measures the impact to the confidentiality of information
Integrity Impact
High (H)
Measures the impact to integrity of a successfully exploited vulnerability
Availability Impact
High (H)
Measures the impact to the availability of the impacted component
Description
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.