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CVE-2026-69258 – Unauthenticated Property Injection – Flowise ≤ 3.1.2

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Summary

CVE-2026-69258 is an unauthenticated property injection vulnerability in Flowise, the open-source generative AI agent building platform. The POST /api/v1/prediction/:id prediction endpoint spreads an attacker-supplied overrideConfig object into internal execution objects without authorization checks, allowing manipulation of a public chatflow’s execution context. It affects Flowise versions up to and including 3.1.2 and is rated High severity (CVSS 8.8).

Technical details

  • Root cause: The prediction endpoint takes the incomingInput.overrideConfig object and spreads it directly into flowConfig and flowData with no gating and without verifying apiOverrideStatus (buildChatflow.ts and index.ts).
  • Trigger conditions: A public (unauthenticated) chatflow receiving a crafted POST request whose overrideConfig contains injected properties.
  • Attack vector: Network, no authentication, low complexity.
  • Impact: Attackers can inject arbitrary properties into the flow execution context — overriding values such as chatId, sessionId, and chatHistory (enabling session manipulation and injected chat history), and defining properties that become accessible to node templates via $flow.* variable references.
  • Classification: CWE-639 (Authorization Bypass Through User-Controlled Key) and CWE-915 (Improperly Controlled Modification of Dynamically-Determined Object Attributes).

Affected software

  • Flowise (npm flowise) versions ≤ 3.1.2.

Severity

  • CVSS v4.0 base score: 8.8 (High)
  • Vector: CVSS:4.0/AV:N/AC:L/AT:N/PR:N/UI:N/VC:L/VI:H/VA:N/SC:N/SI:N/SA:N

Mitigation and recommended actions

  • Immediate: Upgrade to Flowise 3.1.3, which replaces the ungated spread with explicit property handling.
  • If no patch: Restrict network access to the prediction API, avoid exposing public/unauthenticated chatflows to the internet, and enforce authentication on prediction endpoints. Where feasible, restrict or allowlist permitted overrideConfig properties and block overrides of sensitive fields such as chatId, sessionId, chatHistory, and apiMessageId.

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.

  • Page title: Flowise - Build AI Agents, Visually, Flowise - Low-code LLM apps builder
  • A meta tag’s content: FlowiseAI, Open source generative AI development platform for building AI agents
  • Raw response body: data-rewardful="9a3a26", og:site_name … flowiseai.com

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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