CVE-2026-107717

Published: Ott 09, 2026 Last Modified: Ott 09, 2026

ExploitDB:

Other exploit source:

Google Dorks:

MEDIUM 6,5

Source: [email protected]

Attack Vector: network

Attack Complexity: low

Privileges Required: none

User Interaction: none

Scope: unchanged

Confidentiality: low

Integrity: low

Availability: none

Description

AI Translation Available

Banks generates meaningful LLM prompts using a simple template language. Prior to 2.5.0, Banks Prompt.chat_messages() attempts to parse every line of rendered template output as ChatMessage JSON. When an application renders untrusted data and passes the returned ChatMessage objects to an LLM provider, attacker-controlled JSON can cross the prompt boundary and become a system, assistant, or tool message because ChatMessage.role accepts arbitrary strings. This can override application instructions, alter the intended prompt structure, or confuse downstream tool and message handling. This issue is fixed in version 2.5.0.

EPSS (Exploit Prediction Scoring System)

Trend Analysis

EPSS (Exploit Prediction Scoring System)

Prevede la probabilità di sfruttamento basata su intelligence sulle minacce e sulle caratteristiche della vulnerabilità.

EPSS Score

0,0028

Percentile

0,2th

Updated

EPSS Score Trend (Last 3 Days)

Improper Input Validation

Stable

Abstraction: Class Structure: Simple

Common Consequences

Security Scopes Affected:

Availability Confidentiality Integrity

Potential Impacts:

Dos: Crash, Exit, Or Restart Dos: Resource Consumption (Cpu) Dos: Resource Consumption (Memory) Read Memory Read Files Or Directories Modify Memory Execute Unauthorized Code Or Commands