A flaw in ChatGPT’s code execution environment shows how a single malicious prompt could quietly leak sensitive user data — without any warning or user approval needed.
“Sensitive data shared with ChatGPT conversations could be silently exfiltrated without the user’s knowledge or approval,” said Check Point researchers.
Inside the ChatGPT DNS Exfiltration Flaw
The issue exposes a critical gap in how AI platforms secure sensitive data within execution environments.
ChatGPT’s Python-based runtime is designed with safeguards that restrict direct outbound internet access and require explicit user approval before any data is shared through external integrations.
However, researchers identified a workaround that bypasses these controls, allowing data to leave the environment through an unintended communication channel.
According to Check Point researchers, the vulnerability could enable the silent exposure of user inputs, uploaded files, and even model-generated outputs.
How the Attack Works
The attack itself is relatively simple to initiate, requiring only a single malicious prompt embedded in a conversation or delivered through a custom GPT.
Once triggered, each subsequent interaction becomes a potential source of data leakage, all without user awareness or visible indicators.
DNS Tunneling Enables Covert Data Exfiltration
Because the exploit operates within the execution runtime, it bypasses the safeguards that would normally block or require approval for outbound data transfers.
At the core of the issue is a side channel that leverages DNS resolution.
While traditional outbound connections are restricted, DNS requests remain functional as part of normal system operations.
Attackers can take advantage of this by encoding sensitive data into DNS queries, which are then transmitted externally during routine name resolution.
This method, known as DNS tunneling, allows data to cross isolation boundaries indirectly and covertly.
In this case, it enabled not only silent data exfiltration but also bidirectional communication.
The researchers demonstrated that attackers could send commands back into the runtime and receive responses, effectively establishing a remote shell within the containerized environment.
Managing Risk in AI Adoption
As organizations rapidly adopt AI tools, sensitive data is increasingly flowing through environments not originally designed for secure handling.
This creates new risks around data exposure, misuse, and unintended leakage, especially as attackers target AI workflows.
Without clear controls and visibility, even routine usage can introduce hidden vulnerabilities.
- Limit sensitive data exposure by enforcing data classification, minimizing inputs, and using data loss prevention solutions.
- Establish and enforce clear AI usage policies, including guidelines for handling confidential data and approved use cases.
- Monitor AI interactions and system activity using logging, behavioral analytics, and anomaly detection to identify potential misuse.
- Restrict and review custom GPTs, prompts, and integrations to prevent malicious logic or unauthorized data access.
- Strengthen runtime and network controls by validating isolation boundaries and monitoring DNS or other outbound channels.
- Segment sensitive workflows and apply least privilege access to reduce the risk of lateral movement or data exposure.
- Test incident response plans around AI data leakage and compromise scenarios.
These measures help organizations build resilience and reduce exposure.
AI Expands the Attack Surface
This incident reflects how AI platforms are becoming more complex execution environments, expanding the attack surface in ways that aren’t always obvious.
It also shows how overlooked components like DNS can introduce risk if not considered in security models.
As organizations rely more on AI for critical workflows, improving visibility, strengthening isolation, and validating data flows will be key to reducing exposure.
Organizations are turning to zero trust solutions to address this challenge by continuously verifying access, minimizing implicit trust, and enforcing tighter control over data movement in AI-driven environments.