Full Report
Some of the latest, best features of ChatGPT can be twisted to make indirect prompt injection (IPI) attacks more severe than they ever were before. That’s according to researchers from Radware, who have created a new exploit chain it calls “ZombieAgent,” which demonstrates not merely that ChatGPT is vulnerable to IPI, but that its new connector and memory…
Analysis Summary
# Vulnerability: Indirect Prompt Injection Enhanced by ChatGPT Memory and Connectors (ZombieAgent)
## CVE Details
- CVE ID: Not specified in the provided text. (This appears to be a conceptual exploit chain targeting architectural features rather than a specific, tracked CVE at the time of reporting.)
- CVSS Score: Not specified.
- CWE: Related to improper input validation/injection, likely CWE-74 (Improper Neutralization of Special Elements in Output Used by a Web Page Subsystem) or similar prompt injection CWEs.
## Affected Systems
- Products: ChatGPT (Specifically features like Connectors and Long-Term Memory).
- Versions: Undetermined, but specifically targets the "latest, best features" including connectors and memory functionality.
- Configurations: Any configuration utilizing ChatGPT's new connector integrations or enabling its long-term memory feature.
## Vulnerability Description
Researchers from Radware developed an exploit chain named "ZombieAgent" demonstrating that recent ChatGPT features significantly escalate the severity of Indirect Prompt Injection (IPI) attacks. This is achieved by weaponizing two new features:
1. **Connectors:** Integrations linking ChatGPT with external software platforms (e.g., mail services, productivity tools).
2. **Long-Term Memory:** A feature allowing information fed to ChatGPT to persist and influence future outputs indefinitely.
The combination allows IPI attacks to become more persistent and widespread by leveraging external data sources and maintaining malicious instructions within the model's memory.
## Exploitation
- Status: Demonstrated via Proof of Concept (PoC) by Radware researchers.
- Complexity: Implied to be heightened due to the persistence and breadth offered by memory and connectors.
- Attack Vector: Relies on indirect injection routes (e.g., compromised data sources read by ChatGPT via connectors) coupled with persistent malicious memory storage.
## Impact
- Confidentiality: Potential for data exfiltration or exposure if connectors access sensitive information.
- Integrity: High potential if persistent instructions manipulate future outputs or actions taken by connected tools.
- Availability: Potential for service disruption depending on the specific functions targeted by the injection payload.
## Remediation
### Patches
- No specific patch versions are listed, as the report focuses on the exploit demonstration. Remediation would require updates from the LLM provider (presumably OpenAI) to address input sanitization, memory management isolation, and connector trust boundaries.
### Workarounds
- Organizations may consider limiting or disabling ChatGPT Connector functionality if feasible.
- Be highly cautious of data stored in the model's long-term memory, particularly data sourced from untrusted or external domains.
## Detection
- Indicators of Compromise (IOCs) are not listed, but detection should focus on:
- Monitoring the data sources feeding into ChatGPT utilized via connectors for unexpected external data insertion.
- Auditing ChatGPT history/memory logs for persistent, unusual, or system-level instructions.
- Behavioral analysis flagging outputs that deviate substantially from typical responses or attempt to interact repeatedly with connected services outside normal parameters.
## References
- Radware Research: hxxps://www.radware.com/blog/threat-intelligence/zombieagent/
- Dark Reading Reference: hxxps://www.darkreading.com/endpoint-security/chatgpt-memory-feature-prompt-injection