CVE-2025-62373: Pipecat Deserialization Flaw - What It Means for Your Business and How to Respond

Business leaders in the USA and Canada increasingly adopt AI-driven voice agents for customer service, sales automation, and internal operations to stay competitive. CVE-2025-62373 exposes a critical remote code execution vulnerability in Pipecat, an open-source Python framework popular for building real-time voice and multimodal conversational AI agents. Companies using this framework, especially those integrating it with WebSocket services for live interactions, face severe risks from attackers who can run arbitrary code on your servers. This post explains the business implications, helps you assess exposure, and outlines response steps, with technical details reserved for your security team in the appendix.

S1 — Background & History

CVE-2025-62373 was publicly disclosed on April 23, 2026, through the National Vulnerability Database after GitHub, Inc. reported it via their security advisory process. The vulnerability affects Pipecat, a Python framework used to develop real-time voice and multimodal AI agents, specifically versions 0.0.41 to 0.0.93. In plain terms, it stems from improper handling of data sent over WebSocket connections, allowing attackers to inject and execute harmful code remotely without authentication.

The CVSS v3.1 base score stands at 9.8, classifying it as critical severity due to its network accessibility, low complexity, and high impact on confidentiality, integrity, and availability. Key timeline events include the advisory publication on GitHub around April 24, 2026, followed by NVD entry the same day, and Pipecat releasing version 0.0.94 with a fix shortly after. No prior exploitation in the wild has been reported as of May 2026, but the ease of attack prompts immediate attention for exposed deployments.

S2 — What This Means for Your Business

You rely on AI voice agents powered by frameworks like Pipecat to handle customer calls, process transactions, and automate workflows, giving you an edge in efficiency. An exploit of CVE-2025-62373 lets remote attackers execute code on your servers, potentially stealing sensitive customer data such as payment details or personal information stored in agent memory. This leads to direct financial losses from theft or ransomware, plus operational downtime if attackers crash services or deploy malware.

Your reputation suffers when breaches become public, eroding customer trust in your brand, especially in regulated sectors like finance or healthcare where voice AI manages compliance-sensitive interactions. Compliance risks escalate too: in the USA, violations of laws like the California Consumer Privacy Act or Health Insurance Portability and Accountability Act could trigger fines up to millions, while Canada's Personal Information Protection and Electronic Documents Act imposes similar penalties for data mishandling. You face legal liabilities, regulatory audits, and higher insurance premiums, all compounding recovery costs that divert resources from growth. Proactive assessment now prevents these cascading effects.

S3 — Real-World Examples

Regional Bank Call Center Breach: Your AI voice agents using Pipecat handle loan inquiries and account verification for thousands daily. An attacker exploits the flaw over WebSocket, extracts customer financial data, and sells it on the dark web. You incur millions in breach notification costs and lose clients to competitors.

Healthcare Provider Patient Triage Failure: You deploy Pipecat-based bots for appointment scheduling and symptom triage in your clinics. Remote code execution lets hackers alter patient records or disrupt service, delaying care and exposing protected health information. Regulators fine you heavily under HIPAA, and lawsuits follow from affected patients.

Mid-Size Retailer Sales Automation Halt: Your e-commerce voice assistants powered by Pipecat process orders via phone. Attackers gain server control, injecting fake transactions or wiping order histories. Sales drop 30% during downtime, and supply chain partners sue over lost revenue.

Enterprise Contact Center Outage: You integrate Pipecat for multilingual support in your North American operations center. A crafted payload crashes all instances, silencing customer service for days. Stock prices dip, and you spend on expedited fixes while competitors capture market share.

S4 — Am I Affected?

• You are running Pipecat versions 0.0.41 through 0.0.93 in any production environment.

• Your developers configured LivekitFrameSerializer for LiveKit integration in voice AI agents.

• Your Pipecat servers listen on external interfaces like 0.0.0.0 or public IPs, exposing WebSocket endpoints to the internet.

• You use Pipecat for real-time voice or multimodal agents handling customer interactions without network access controls.

• Your containerized or cloud deployments (AWS, Azure, GCP) of Pipecat lack WebSocket traffic restrictions from untrusted sources.

• You have not audited custom Pipecat pipelines for deprecated serializers since April 2026.

Key Takeaways

• CVE-2025-62373 allows remote attackers to run arbitrary code on Pipecat servers via unsafe data deserialization over WebSocket.

• Businesses face data theft, service outages, reputational damage, and compliance fines from exploitation.

• Check if you use affected Pipecat versions 0.0.41-0.0.93 with LivekitFrameSerializer exposed externally.

• Upgrade to 0.0.94 immediately and disable vulnerable components to block risks.

• Engage penetration testing to uncover hidden exposures in AI deployments.

Call to Action

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TECHNICAL APPENDIX (security engineers, pentesters, IT professionals only)

A — Technical Analysis

The root cause lies in the LivekitFrameSerializer class's deserialize() method, which invokes Python's pickle.loads() directly on untrusted WebSocket client data without validation, enabling deserialization of untrusted data (CWE-502). This affects the optional, deprecated serializer in src/pipecat/serializers/livekit.py around line 73, used for LiveKit frame handling in Pipecat versions 0.0.41-0.0.93. Attackers connect via WebSocket to a network-exposed server (low complexity, no privileges or user interaction needed) and send a crafted pickle payload for remote code execution.

CVSS vector is CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:H (score 9.8). See NVD reference at https://nvd.nist.gov/vuln/detail/CVE-2025-62373 and GitHub advisory GHSA-c2jg-5cp7-6wc7.

B — Detection & Verification

Version Check:

• pip show pipecat-ai reveals versions 0.0.41-0.0.93.

• Scan code: grep -r "LivekitFrameSerializer" /path/to/pipecat/install.

Network Indicators:

• WebSocket connections to ws://[host]:[port] with unusual payloads (e.g., pickle gadgets via Wireshark).

• Scanner signatures: Nuclei template for pickle RCE or custom Burp Suite extension for WebSocket pickle detection.

Behavioral Anomalies:

• Sudden processes spawned on Pipecat host (e.g., ps aux | grep unexpected post-WebSocket traffic).

• Logs show deserialization errors or high CPU from pickle handling in Pipecat output.

C — Mitigation & Remediation

1. Immediate (0–24h): Disable LivekitFrameSerializer in configs; firewall WebSocket ports (e.g., iptables -A INPUT -p tcp --dport [port] -s trusted_ips -j ACCEPT; -j DROP); restart services.

2. Short-term (1–7d): pip install pipecat-ai>=0.0.94 --upgrade; migrate to LiveKitTransport; audit deployments with docker ps | grep pipecat or Kubernetes manifests. 

3. Long-term (ongoing): Enforce network segmentation (e.g., VPC peering in AWS); integrate WAF rules for pickle signatures; continuous scanning with tools like Trivy or Snyk; avoid pickle in untrusted deserialization.

D — Best Practices

• Never deserialize untrusted data with pickle or similar unsafe formats in network-facing code.

• Validate and sanitize all WebSocket inputs using safe formats like JSON or Protocol Buffers.

• Bind servers to localhost (127.0.0.1) or use reverse proxies with authentication for external access.

• Implement runtime serialization checks and gadget blacklisting libraries like pickle5 alternatives.

• Regularly audit open-source dependencies with tools like Dependabot or GitHub Advanced Security.