CVE-2026-40980: VMware Spring AI DoS Vulnerability

CVE-2026-40980 Overview

A resource exhaustion vulnerability exists in VMware Spring AI that allows attackers to cause a denial of service condition through specially crafted PDF files. When the ForkPDFLayoutTextStripper component processes a malicious PDF, it can be tricked into allocating unreasonable amounts of memory, potentially exhausting available system resources and causing application crashes or service unavailability.

Critical Impact

Applications using Spring AI for PDF processing are vulnerable to denial of service attacks. An authenticated attacker can upload a malicious PDF file that triggers excessive memory allocation, potentially bringing down AI-powered services that rely on document processing capabilities.

Affected Products

• VMware Spring AI 1.0.0 - 1.0.5
• VMware Spring AI 1.1.0 - 1.1.4

Discovery Timeline

• 2026-04-28 - CVE-2026-40980 published to NVD
• 2026-04-29 - Last updated in NVD database

Technical Details for CVE-2026-40980

Vulnerability Analysis

This vulnerability is classified as CWE-400 (Uncontrolled Resource Consumption), a common weakness pattern where software does not properly control the allocation and maintenance of limited resources. In the context of Spring AI, the ForkPDFLayoutTextStripper component fails to adequately validate or limit resource consumption when parsing PDF documents.

The attack is network-accessible and requires low privileges, meaning any authenticated user with the ability to submit PDF files for processing can exploit this vulnerability. No user interaction is required beyond the initial file submission, making automated exploitation feasible.

Root Cause

The root cause lies in insufficient validation of PDF document structures within the ForkPDFLayoutTextStripper class. When processing PDF files, this component allocates memory based on values or structures embedded within the PDF without implementing proper bounds checking or resource limits. A maliciously crafted PDF can specify parameters that cause the parser to attempt massive memory allocations, bypassing normal operating constraints.

This type of vulnerability often stems from trusting user-supplied data within document formats. PDF files can contain complex nested structures, embedded objects, and metadata that parsers must process. Without careful resource management, an attacker can exploit these parsing routines to trigger denial of service conditions.

Attack Vector

The attack vector involves crafting a malicious PDF file designed to exploit the memory allocation behavior of ForkPDFLayoutTextStripper. An attacker with low-level privileges on a system running a vulnerable Spring AI application can:

1. Create a PDF file with specially crafted structures that trigger excessive memory allocation
2. Submit the malicious PDF to any endpoint that processes documents using the vulnerable component
3. Cause the application to exhaust available memory, leading to service degradation or complete denial of service

The vulnerability is particularly concerning for AI-powered document processing pipelines where PDFs are commonly ingested for text extraction, analysis, or embedding generation. The attack does not compromise data confidentiality or integrity but can significantly impact service availability.

Detection Methods for CVE-2026-40980

Indicators of Compromise

• Abnormal memory consumption spikes correlated with PDF upload or processing events
• Application crashes or out-of-memory errors in services utilizing Spring AI document processing
• Unusual PDF file submissions with anomalous file structures or sizes
• Repeated memory allocation failures logged by the JVM or container runtime

Detection Strategies

• Monitor JVM heap usage and garbage collection patterns for anomalies during PDF processing operations
• Implement application-level logging to track memory allocation during ForkPDFLayoutTextStripper operations
• Deploy file integrity monitoring to flag suspiciously structured PDF uploads before processing
• Configure alerting thresholds for memory utilization on services running Spring AI components

Monitoring Recommendations

• Establish baseline memory consumption metrics for PDF processing workloads
• Implement rate limiting on document upload endpoints to reduce exploitation impact
• Enable detailed logging for the Spring AI PDF processing pipeline to capture resource utilization data
• Consider deploying container resource limits to prevent memory exhaustion from affecting other services

How to Mitigate CVE-2026-40980

Immediate Actions Required

• Upgrade Spring AI to version 1.0.6 or later for the 1.0.x branch
• Upgrade Spring AI to version 1.1.5 or later for the 1.1.x branch
• Review application logs for evidence of exploitation attempts
• Implement memory limits on processes handling PDF uploads as a temporary safeguard

Patch Information

VMware has released patched versions that address this vulnerability:

For complete patch details and upgrade instructions, refer to the Spring Security Advisory for CVE-2026-40980.

Workarounds

• Implement strict file size limits on PDF uploads before they reach the Spring AI processing pipeline
• Add pre-processing validation to reject PDFs with suspicious structural characteristics
• Deploy resource quotas (memory limits) on containers or JVMs running Spring AI services
• Consider temporarily disabling PDF processing functionality if the component is not critical

# Example: Configure JVM memory limits for Spring AI applications
# Add to application startup script or container configuration
export JAVA_OPTS="-Xmx2g -XX:MaxMetaspaceSize=256m -XX:+HeapDumpOnOutOfMemoryError"

# Example: Container memory limits in Kubernetes deployment
# spec.containers[].resources.limits.memory: "2Gi"
# spec.containers[].resources.requests.memory: "1Gi"