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A vulnerability has been identified in OpenClaw’s AI assistant that could allow attackers to insert crafted content into system logs.
The flaw stems from how certain WebSocket headers were logged, creating a potential log poisoning risk in AI-assisted workflows.
“This issue is primarily an indirect prompt injection risk and depends on downstream log consumption behavior. If you do not feed logs into an LLM or other automation, impact is limited,” said OpenClaw in its advisory.
How the OpenClaw Log Poisoning Flaw Works
The vulnerability originates in OpenClaw’s gateway server component (src/gateway/server/ws-connection.ts) and affects versions up to and including 2026.2.12. It has been resolved in version 2026.2.13.
In the affected releases, when a WebSocket connection closed before completing the handshake process, certain request headers — such as Origin and User-Agent — were logged without sanitization or length restrictions.
As a result, user-controlled header values could be written directly into structured log entries.
If an unauthenticated attacker is able to reach the OpenClaw gateway interface, they could submit specially crafted header values that would then appear verbatim in the logs.
Although this does not allow remote code execution (RCE) or bypass authentication controls, it introduces a risk of indirect manipulation.
The concern arises when those logs are later used as context for large language model (LLM) reasoning, such as in AI-assisted debugging workflows.
In that scenario, injected content could be misinterpreted as legitimate system output, operator guidance, or structured diagnostic information.
The overall impact depends on how logs are consumed downstream. If logs are used strictly for human review, the practical risk is limited.
However, in environments where AI agents automatically ingest log data to troubleshoot or summarize activity, poisoned entries could influence how the model interprets events, frames conclusions, or recommends next steps.
At the time of disclosure, there were no reports of active exploitation or publicly available proof-of-concept code.
Mitigate OpenClaw Log Poisoning Risk
Addressing this vulnerability requires both patching and a broader review of how logs and gateway access are managed.
Because the risk is tied to how untrusted input may later influence AI reasoning, organizations should evaluate their logging, exposure, and monitoring practices.
- Patch to the latest OpenClaw version and verify that WebSocket header values are properly sanitized before being written to logs.
- Restrict gateway exposure by removing public internet access, enforcing strong authentication, and applying firewall, VPN, or zero-trust access controls.
- Treat logs as untrusted input by sanitizing and encoding user-controlled fields, imposing header length limits, and preventing raw telemetry from being directly ingested by AI reasoning workflows.
- Separate debugging logs from AI-consumable inputs and implement filtering or guardrails to reduce the risk of indirect prompt injection.
- Monitor for abnormal header patterns, spikes in failed WebSocket connections, and unusual AI outputs that could indicate log poisoning attempts.
- Apply rate limiting, IP allowlisting, and web application firewall rules to reduce the ability of attackers to repeatedly inject crafted requests.
- Test incident response plans that include playbooks for investigating suspicious log activity and validating the integrity of AI-driven troubleshooting outputs.
These measures can help reduce log poisoning risk and strengthen trust boundaries in AI-assisted OpenClaw deployments.
Logs in AI Environments
The OpenClaw log poisoning vulnerability highlights how AI-integrated systems introduce new security considerations beyond just traditional exploits.
Even when no direct code execution risk exists, the way data is logged and later interpreted by language models can create unintended trust pathways.
As organizations continue embedding AI assistants into operational workflows, they must treat logs and telemetry as untrusted inputs and reassess how automated reasoning systems consume contextual data.
These trust boundary challenges are one reason organizations are leveraging zero-trust principles to enforce continuous verification across users, devices, and data flows.
