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CVE-2026-85663 – Unauthenticated Remote Code Execution / Full Data Compromise – Aim Server ≤3.29.1

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Summary

CVE-2026-85663 is a missing-authentication vulnerability (CWE-306) in the Aim experiment-tracking server (the aim PyPI package), affecting versions up through 3.29.1. The remote tracking server accepts client registrations and dispatches method calls without any authentication, letting unauthenticated network attackers invoke arbitrary server-side methods. The issue is rated critical severity due to its unauthenticated, network-exploitable nature and its impact on confidentiality, integrity, and availability of tracked experiment data.

Technical details

  • Root cause: the Aim remote tracking server does not require authentication for incoming client connections.
  • The server dispatches requested method calls using Python’s getattr mechanism without validating the requested method against an allowlist of permitted operations.
  • This allows a remote, unauthenticated client to register itself with the server, instantiate Repo resources, and invoke arbitrary methods exposed on those objects.
  • Attack vector: network — no privileges or user interaction are required to reach and exploit the tracking server’s listening interface.
  • Impact: attackers can read experiment/run data (confidentiality), delete or tamper with tracked runs (integrity/availability), and potentially trigger other unintended method behavior depending on what is reachable via the unauthenticated dispatch path.

Affected software

  • aim (aimhubio), remote tracking server component — versions 0 through 3.29.1 (all releases up to and including 3.29.1).

Severity

  • CVSS v3.1 Base Score: 9.8 (Critical) — CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:H
  • CVSS v4.0 Base Score: 9.3 (Critical) — CVSS:4.0/AV:N/AC:L/AT:N/PR:N/UI:N/VC:H/VI:H/VA:H/SC:N/SI:N/SA:N

Mitigation and recommended actions

  • Immediate: At the time of publication, no patched Aim release addressing this issue has been identified. Check the aimhubio/aim GitHub releases page regularly for a fix and upgrade as soon as one becomes available.
  • If no patch: Do not expose the Aim remote tracking server directly to untrusted networks or the public internet. Restrict access to the tracking server’s listening port to trusted hosts only, using network-level controls such as firewall rules, VPN, or an authenticating reverse proxy in front of the service.
  • Monitor Aim tracking servers for unexpected client registrations, unusual method invocations, or unexplained deletion/modification of run data.

How IONIX identifies potentially affected assets

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

  • Raw response body: Open-source, self-hosted AI experiment tracking tool, href="assets/icomoon/icomoonIcons.css

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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