Evidence used
- No CISA KEV confirmation is currently recorded.
- EPSS is 0.44% for the current model date.
BlackTreeCVE IntelligenceSignificant-Gravitas · Auto-GPT
Official source article: GitHub GHSA-R7F7-QRRV-3FJH ↗. Check the applicable product and release in the original source.
Low technical severity with no CISA KEV confirmation; remediate through the normal risk-based patch cycle unless local exposure raises the priority.
Low technical severity with no CISA KEV confirmation; remediate through the normal risk-based patch cycle unless local exposure raises the priority.
Patch availableAuto-GPT is an experimental open-source application showcasing the capabilities of the GPT-4 language model. The Auto-GPT command line UI makes heavy use of color-coded print statements to signify different types of system messages to the user, including messages that are crucial for the user to review and control which commands should be executed. Before v0.4.3, it was possible for a malicious external resource (such as a website browsed by Auto-GPT) to cause misleading messages to be printed to the console by getting the LLM to regurgitate JSON encoded ANSI escape sequences (`\u001b[`). These escape sequences were JSON decoded and printed to the console as part of the model's "thinking process". The issue has been patched in release version 0.4.3.
Auto-GPT is an experimental open-source application showcasing the capabilities of the GPT-4 language model. The Auto-GPT command line UI makes heavy use of color-coded print statements to signify different types of system messages to the user, including messages that are crucial for the user to review and control which commands should be executed. Before v0.4.3, it was possible for a malicious external resource (such as a website browsed by Auto-GPT) to cause misleading messages to be printed to the console by getting the LLM to regurgitate JSON encoded ANSI escape sequences (`\u001b[`). These escape sequences were JSON decoded and printed to the console as part of the model's "thinking process". The issue has been patched in release version 0.4.3.
The product constructs a log message from external input, but it does not neutralize or incorrectly neutralizes special elements when the message is written to a log file.
An attacker operating through a network path may attempt exploitation without authentication after a user interaction. If successful, the issue may cause the confidentiality, integrity or availability impact described by the vendor.
Auto-GPT is an experimental open-source application showcasing the capabilities of the GPT-4 language model. The Auto-GPT command line UI makes heavy use of color-coded print statements to signify different types of system messages to the user, including messages that are crucial for the user to review and control which commands should be executed. Before v0.4.3, it was possible for a malicious external resource (such as a website browsed by Auto-GPT) to cause misleading messages to be printed to the console by getting the LLM to regurgitate JSON encoded ANSI escape sequences (`\u001b[`). These escape sequences were JSON decoded and printed to the console as part of the model's "thinking process". The issue has been patched in release version 0.4.3.
The product constructs a log message from external input, but it does not neutralize or incorrectly neutralizes special elements when the message is written to a log file.
An attacker operating through a network path may attempt exploitation without authentication after a user interaction. If successful, the issue may cause the confidentiality, integrity or availability impact described by the vendor.
CVSS severity, EPSS forecast probability, public exploit material and CISA-confirmed exploitation are separate signals.
No CISA KEV match was present at the last successful refresh. This means no confirmation from that source, not proof of no exploitation.
No exploit-tagged reference or CISA SSVC proof-of-concept state is currently recorded. Research may still exist outside the structured feeds.
CWE-117: Improper Output Neutralization for Logs. The product constructs a log message from external input, but it does not neutralize or incorrectly neutralizes special elements when the message is written to a log file.
CVSS:3.1/AV:N/AC:H/PR:N/UI:R/S:U/C:N/I:L/A:NCommon Vulnerability Scoring System 3.1: the compact vector below is decoded into plain language.
Operational remediation based on structured source evidence.
Published 13 Jul 2023 · Last source change 22 Oct 2024, 14:52 UTC · CWE-117 · Improper Output Neutralization for Logs
Core structured fields are present and their contributing authorities are shown above.
No material field changes have been recorded since change tracking began. Routine source refreshes and cosmetic edits are intentionally excluded.