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CVE-2026-79696 – Unauthenticated Remote Code Execution – Google Cloud Agent Development Kit (ADK) fo

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Summary

CVE-2026-79696 is a critical code injection vulnerability in the adk web component of Google Cloud’s Agent Development Kit (ADK) for Python, affecting versions 2.0.0 through 2.6.0. When pytest is installed in the runtime (applicable to OSS Python, Cloud Run, and GKE deployments), an unauthenticated remote attacker can achieve arbitrary code execution by submitting a crafted test session replay. The issue carries the maximum CVSS severity rating of 10.0 (Critical).

Technical details

  • Root cause: ADK’s YAML agent configuration loader used an incomplete denylist of blocked Python standard library functions/modules rather than blocking the stdlib as a whole, permitting references to code-executing functions such as cProfile.run, timeit.timeit, and trace.Trace.run.
  • Trigger conditions: an attacker supplies an agent YAML configuration that names one of these code-executing stdlib functions as a "tool," then replays a crafted/saved test session; ADK’s test-replay logic dispatches the recorded function call directly to the resolved tool, executing attacker-controlled code.
  • Attack vector: Network — exploitable without authentication or user interaction against exposed adk web instances where pytest is installed.
  • Impact: full compromise of confidentiality, integrity, and availability on the host running adk web (arbitrary remote code execution).

Affected software

  • Google Cloud Agent Development Kit (ADK) for Python, versions 2.0.0 through 2.6.0
  • Applicable to deployments on OSS Python, Cloud Run, and GKE where pytest is installed alongside ADK

Severity

CVSS v4.0 Base Score: 10.0 (Critical)
Vector: CVSS:4.0/AV:N/AC:L/AT:N/PR:N/UI:N/VC:H/VI:H/VA:H/SC:H/SI:H/SA:H/U:Amber

Mitigation and recommended actions

  • Immediate: Upgrade to google-adk version 2.7.0 or later, which blocks the entire Python standard library from being referenced in agent configurations rather than relying on an incomplete denylist.
  • If patching is not immediately possible: Do not expose adk web to the network; restrict access to trusted, local, or internal-only environments, and avoid running adk web in environments where pytest is installed until upgraded.

How IONIX identifies potentially affected assets

IONIX matches the following signal against data already collected when it crawled the asset; identifying the technology sends no request beyond that crawl.

  • Page title: Agent Development Kit Dev UI

References

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1

Map your entire attack surface (continously)

IONIX uses multi-factor discovery methods, including DNS analysis, certificate mapping, metadata inspection, and more, to automatically map every internet-facing asset across your environment. This includes cloud instances, third-party platforms, shadow IT, and even forgotten infrastructure that traditional tools miss.

2

Monitor for new CVEs

Dozens of threat intel feeds using agentic technology are continuously analyzed to detect the appearance of proof-of-concept code, exploit kits, and indicators of active targeting. IONIX goes further by applying AI to proactively evaluate whether emerging vulnerabilities are likely to be exploited, even before PoCs go public.

3

Identify Potential External Exposures

Not all CVEs matter. IONIX filters vulnerabilities by asking attacker-centric questions: Can it be reached from the internet? Does it require authentication? Is it being exploited in the wild? This dramatically reduces noise and focuses teams on threats that can actually be weaponized.

4

Create Safe, Scalable Exploit Validations

IONIX transforms real-world PoCs into safe, non-intrusive test payloads that can be run in production environments without disruption. These simulations are precisely targeted to the systems that are vulnerable, ensuring rapid validation without unnecessary load.

5

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By combining context about software stack, versioning, exposure status, and reachability, IONIX ensures that only the right payloads are executed against the right assets, maximizing efficiency and minimizing risk.

6

Drive Fast and Actionable Remediation

Results are routed through integrations with ticketing, SOAR, and SIEM tools. Issues are written in plain language, bundled into remediation clusters, and prioritized based on asset criticality, exploitability, and blast radius. This shortens mean time to remediation (MTTR) and empowers teams to act with confidence.

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