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CVE-2026-85664 – Unauthenticated Denial of Service (Memory Exhaustion) – Chroma (chromadb) ≤ 1.5.9

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Summary

CVE-2026-85664 is a resource-allocation vulnerability (CWE-770) in Chroma, the open-source vector database (PyPI package chromadb), affecting version 1.5.9 and earlier. The server fails to enforce upper bounds on HNSW index parameters supplied in collection-create requests, letting an unauthenticated attacker force excessive memory allocation and crash the server during index compaction. The issue carries a High severity rating (CVSS v4.0 base score 8.7).

Technical details

  • Root cause: Chroma’s collection-create API accepts the HNSW index parameters max_neighbors, ef_construction, and ef_search without validating any maximum bound.
  • Trigger condition: An attacker submits a collection-create request with arbitrarily large values for one or more of these parameters.
  • Attack vector: Network-based, requiring no authentication and no user interaction — the endpoint accepts the request pre-auth.
  • Impact: The oversized parameters drive excessive memory allocation during index compaction, exhausting server memory and causing denial of service; confidentiality and integrity are not affected.

Affected software

  • Chroma (chroma-core/chroma, PyPI package chromadb) — all versions up to and including 1.5.9.
  • Versions released after 1.5.9 are marked unaffected by the vendor record.

Severity

  • CVSS v4.0: 8.7 (High) — CVSS:4.0/AV:N/AC:L/AT:N/PR:N/UI:N/VC:N/VI:N/VA:H/SC:N/SI:N/SA:N
  • CVSS v3.1: 7.5 (High) — CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H

Mitigation and recommended actions

  • Immediate: Upgrade Chroma to a version released after 1.5.9, where the unaffected status applies.
  • If unable to patch immediately: Restrict network access to the Chroma API to trusted hosts/networks only, place the service behind an authenticating reverse proxy, and monitor/limit memory usage on the host running the compaction worker to reduce the impact of a triggered exhaustion event.

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.

  • Response header chroma-trace-id: any value present.

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

Execute Exploit Validations

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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