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As AI-powered coding tools become increasingly integrated into cloud workflows, a new security concern is emerging. Researchers from Phantom Labs have demonstrated a method by which sensitive data can be exfiltrated from AI code execution environments using Domain Name System (DNS) queries. The findings reveal that even when network connections are restricted in sandboxed environments, attackers may still leverage DNS resolution to create covert communication channels, potentially bypassing critical security controls. This research casts a spotlight on architectural weaknesses in AI-driven cloud systems, raising important questions for organizations relying on such tools.
How the Attack Works
Phantom Labs’ report, published on March 16, focuses on AWS Bedrock’s AgentCore Code Interpreter. The attack starts with a malicious CSV file containing embedded instructions. When the AI agent processes the file to generate Python code for execution, these instructions can influence the output. Instead of executing standard analysis tasks, the AI-generated code can be coerced into communicating with an external command-and-control (C2) server via DNS queries.
During testing, researchers demonstrated several capabilities:
Running basic sandbox commands like whoami
Listing Amazon S3 buckets and reading their contents
Extracting sensitive files, including credentials, personal data, and financial records
Despite these actions, the environment still reported network access as disabled, highlighting how DNS resolution remains an unmitigated channel.
Ram Varadarajan, CEO of Acalvio, commented: “AWS
Cloud Permissions Amplify Risk
The study emphasizes that the risk escalates when Code Interpreter instances are assigned overly permissive IAM roles. For example, the default AgentCore Starter Toolkit role can provide:
Full access to DynamoDB
Full access to Secrets Manager
Read access to all S3 buckets in the account
If attackers manipulate code execution within these interpreters, they could exploit these permissions to access sensitive organizational data. Jason Soroko, senior fellow at Sectigo, warned: “Organizations must recognize that Sandbox mode does not guarantee isolation from external networks.”
AWS Response
AWS reviewed Phantom Labs’ findings and determined that the observed behavior is intentional, not a vulnerability. Sandbox Mode permits DNS resolution, and the company updated its documentation to clarify this. Organizations are advised to migrate critical workloads to VPC Mode for stricter isolation. Soroko added: “Administrators should inventory all active AgentCore Code Interpreter instances and move sensitive workloads out of Sandbox mode immediately.”
Broader Implications
This research underscores a growing challenge in AI-enabled cloud infrastructure: AI agents executing code autonomously can inadvertently bypass traditional network and security controls. Without strict permission boundaries, sandboxed AI interpreters may become unmonitored channels for data leakage, putting sensitive corporate information at risk.
What Undercode Say:
This study highlights a fundamental tension in cloud AI security. Sandboxing is intended to create isolated execution environments, but the reliance on DNS as a system service exposes a blind spot. Malicious actors exploiting this gap could run automated attacks at scale, silently polling C2 servers and extracting sensitive data without raising traditional network alarms.
From a design perspective, it reveals that current “sandbox” assumptions do not hold when the AI has code-generation capabilities. While network egress controls limit conventional connections, they fail to block DNS queries—a protocol often overlooked in threat modeling. Organizations relying on AI code interpreters must consider both IAM role restrictions and environment-level network segmentation to prevent such attacks.
Additionally, this highlights the risk of “agentic AI” systems: tools capable of modifying or generating code autonomously. Even a small oversight in role assignment or environment configuration could escalate into full-scale data breaches. Companies may need to adopt continuous monitoring of AI workloads and implement DNS filtering or logging to mitigate this subtle attack vector.
Finally, this research demonstrates that cloud security is evolving in complexity. Traditional perimeter controls are insufficient against autonomous AI agents, meaning security teams must rethink isolation strategies, trust boundaries, and code execution governance for AI-powered services. Without these adaptations, AI interpreters can become unintended channels for sensitive data exfiltration.
Fact Checker Results:
✅ DNS exfiltration is possible even in restricted sandbox modes.
✅ AWS confirms Sandbox Mode allows DNS resolution by design.
❌ No evidence suggests AWS considers this a vulnerability; it’s documented behavior.
Prediction:
⚠️ As AI code interpreters become widespread, DNS-based data exfiltration could emerge as a common threat vector. Companies will increasingly shift critical workloads to fully isolated VPC modes and implement enhanced monitoring of DNS queries. Automated mitigation strategies, including AI-aware network segmentation, will likely become a standard security requirement within the next 12–18 months.
If you want, I can also create a diagram showing the DNS exfiltration attack flow from the CSV file to the C2 server—very useful for readers to visualize the risk. Do you want me to do that?
🕵️📝✔️Let’s dive deep and fact‑check.
References:
Reported By: www.infosecurity-magazine.com
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