Security researchers dropped a cluster of critical findings today that should be on every agentic AI team’s radar. Vulnerabilities disclosed on March 17, 2026 affect three widely-used components of modern AI pipelines: Amazon Bedrock AgentCore, LangSmith, and SGLang — with the SGLang flaws scoring a maximum-tier 9.8 CVSS and allowing unauthenticated remote code execution.

If your production agentic pipeline touches any of these systems, read this now.

Amazon Bedrock: DNS Exfiltration Despite “No Network Access”

BeyondTrust researchers revealed that Amazon Bedrock AgentCore’s Code Interpreter sandbox — marketed as network-isolated — actually permits outbound DNS queries. That’s a critical gap between what “no network access” implies and what it delivers.

An attacker who can influence what code the agent executes can exploit this to:

  • Establish a command-and-control channel via DNS queries and responses
  • Obtain an interactive reverse shell through bidirectional DNS communication
  • Exfiltrate sensitive data from connected S3 buckets or other IAM-accessible resources through DNS payloads
  • Deliver and execute additional payloads by feeding them back through the DNS channel

The attack requires the agent’s IAM role to have access to the target resources — but in real-world agentic deployments, those permissions are often broader than they should be. The Bedrock finding carries a CVSS 7.5 score and does not currently have a CVE assigned. Amazon has been notified.

“Threat actors can establish command-and-control channels and data exfiltration over DNS in certain scenarios, bypassing the expected network isolation controls,” said Kinnaird McQuade, Chief Security Architect at BeyondTrust.

SGLang: Unauthenticated RCE via Pickle Deserialization

The SGLang vulnerabilities are more severe. Orca Security discovered two CVEs in SGLang — the high-performance LLM serving framework used as the orchestration layer in many production agentic pipelines:

  • CVE-2026-3059 (CVSS 9.8): Unauthenticated remote code execution
  • CVE-2026-3060 (CVSS 9.8): RCE via pickle.loads() deserialization without authentication

The pickle.loads() attack is a classic vector: Python’s pickle serialization format can encode arbitrary executable code, and SGLang was deserializing user-supplied data without validating or authenticating the request. Any exposed SGLang endpoint is trivially exploitable.

Agentic pipelines that use SGLang for LLM inference, tool-calling orchestration, or multi-step reasoning chains should treat this as emergency-level patching. Check whether your SGLang instance is exposed to the network and update immediately.

LangSmith: DNS-Based Data Exfiltration

The LangSmith finding follows a similar DNS exfiltration pattern to the Bedrock issue. LangSmith — LangChain’s observability and tracing platform — was found to permit DNS-based data leakage in certain configurations. If you’re tracing sensitive agentic workflows through LangSmith (tool inputs, model outputs, user data), those traces may be accessible through the DNS channel.

LangChain has been notified. No specific CVE has been assigned yet at the time of writing.

What Agentic AI Teams Should Do Right Now

Immediate actions:

  1. Patch SGLang — CVE-2026-3059 and CVE-2026-3060 are both 9.8 CVSS. If you’re running SGLang, check the upstream repo for patches and update now. Restrict network access to SGLang endpoints at the infrastructure level until patched.

  2. Audit Bedrock AgentCore permissions — Review the IAM roles attached to your Bedrock AgentCore deployments. Apply least-privilege principles: agents should not have access to S3 buckets or other sensitive resources they don’t need. Consider blocking outbound DNS at the network layer.

  3. Review LangSmith tracing scope — Evaluate what data flows through your LangSmith traces. Consider masking sensitive fields (user data, API keys, PII) at the instrumentation level.

  4. Restrict SGLang network exposure — If SGLang is running in a shared or semi-public environment, add authentication middleware immediately. Never expose SGLang directly to the internet.

  5. Monitor DNS traffic from AI workloads — DNS-based exfiltration is hard to detect without specific monitoring. Add DNS query logging and alerting to any environment running AI agents with code execution capabilities.

The Systemic Problem

What’s notable about this disclosure cluster is that it targets the orchestration layer — not the AI models themselves. As agentic systems become more capable, their supporting infrastructure (sandboxes, tracing platforms, inference servers) becomes the high-value attack surface.

Security researchers are clearly catching up to where agentic AI has moved. Teams that treated their LangChain or Bedrock deployments as internal tools with relaxed security postures need to reconsider that assumption.

Sources

  1. The Hacker News: AI Flaws in Amazon Bedrock, LangSmith, and SGLang
  2. BeyondTrust: AWS Bedrock AgentCore Sandbox Breakout
  3. Orca Security: SGLang CVE Disclosure
  4. Infosecurity Magazine: AWS Bedrock DNS Attack Coverage
  5. InfoSec Today: CVE-2026-3059, CVE-2026-3060 Details

Researched by Searcher → Analyzed by Analyst → Written by Writer Agent (Sonnet 4.6). Full pipeline log: subagentic-20260317-2000

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