CVE-2025-25362: spacy-llm: SSTI allows unauthenticated RCE (CVSS 9.8)

Q: Is CVE-2025-25362 actively exploited?

Proof-of-concept exploit code is publicly available for CVE-2025-25362, increasing the risk of exploitation.

Q: How to fix CVE-2025-25362?

1. PATCH: Upgrade spacy-llm to 0.7.3 — the fix is available in the referenced commit (8bde049). No API changes required. 2. WORKAROUND (if patching is blocked): Treat the template field as untrusted input — strip or reject any Jinja2/template syntax characters ({%, {{, }}) before passing data to spacy-llm. 3. AUDIT: Search codebases for uses of spacy-llm's template parameter accepting request body data, file contents, or any externally-sourced strings. 4. DETECT: Monitor for anomalous process spawning from Python interpreters running spaCy — unexpected subprocesses, outbound connections from ML worker nodes, or unusual file access patterns. 5. CONTAIN: If exposure is confirmed, rotate all credentials accessible to the compromised process (LLM API keys, DB credentials, cloud IAM tokens).

Q: What systems are affected by CVE-2025-25362?

This vulnerability affects the following AI/ML architecture patterns: NLP preprocessing pipelines, RAG ingestion pipelines, document processing APIs, LLM integration frameworks, agent frameworks with NLP components.

Q: What is the CVSS score for CVE-2025-25362?

CVE-2025-25362 has a CVSS v3.1 base score of 9.8 (CRITICAL). The EPSS exploitation probability is 0.80%.

GHSA-793v-gxfp-9q9hCRITICALPoC AVAILABLECISA: ATTEND

Published March 5, 2025

CISO Take

Any application using spacy-llm <= 0.7.2 that accepts external input to its template field is fully compromised — no authentication required. Upgrade to 0.7.3 immediately; this is a drop-in patch with no breaking changes. Audit every service in your AI/NLP stack that uses spacy-llm and assume breach if template fields were exposed to untrusted input.

What is the risk?

CVSS 9.8 with AV:N/AC:L/PR:N/UI:N represents worst-case exploitability — no authentication, no user interaction, low complexity, full CIA impact. EPSS of 0.00453 reflects low observed exploitation at time of scoring, but a detailed public PoC blog was published on April 1, 2025, substantially lowering the bar for exploitation. AI/ML workloads typically run with broad filesystem and network access, making post-exploitation impact significantly higher than a standard application RCE.

What systems are affected?

Package	Ecosystem	Vulnerable Range	Patched
spaCy	pip	<= 0.7.2	`0.7.3`
34.0KOpenSSF 5.1148 dependentsPushed 10d ago100% patched~52d to patchFull package profile →

Do you use spaCy? You're affected.

How severe is it?

CVSS 3.1

9.8 / 10

EPSS

0.8%

chance of exploitation in 30 days

Higher than 57% of all CVEs

Source: EPSS v3 — FIRST.org

Exploitation Status

Exploit Available

Exploitation: MEDIUM

Sophistication

Trivial

Exploitation Confidence

medium

○CISA SSVC: Public PoC

○Public PoC indexed (trickest/cve)

Composite signal derived from CISA KEV, VulnCheck KEV, CISA SSVC, EPSS, Metasploit, Exploit-DB, trickest/cve, Nuclei templates, and inthewild.io exploitation reports.

What is the attack surface?

AVNetwork

ACLow

PRNone

UINone

SUnchanged

CHigh

IHigh

AHigh

What should I do?

5 steps

PATCH
Upgrade spacy-llm to 0.7.3 — the fix is available in the referenced commit (8bde049). No API changes required.
WORKAROUND (if patching is blocked): Treat the template field as untrusted input — strip or reject any Jinja2/template syntax characters ({%, {{, }}) before passing data to spacy-llm.
AUDIT
Search codebases for uses of spacy-llm's template parameter accepting request body data, file contents, or any externally-sourced strings.
DETECT
Monitor for anomalous process spawning from Python interpreters running spaCy — unexpected subprocesses, outbound connections from ML worker nodes, or unusual file access patterns.
CONTAIN
If exposure is confirmed, rotate all credentials accessible to the compromised process (LLM API keys, DB credentials, cloud IAM tokens).

What does CISA's SSVC say?

DecisionAttend

Exploitationpoc

AutomatableYes

Technical Impacttotal

Source: CISA Vulnrichment (SSVC v2.0). Decision based on the CISA Coordinator decision tree.

How is it classified?

Code ExecutionData ExtractionFrameworkAML.T0010.001- AI Software AML.T0025- Exfiltration via Cyber Means AML.T0049- Exploit Public-Facing Application AML.T0050- Command and Scripting Interpreter

Which compliance frameworks are affected?

This CVE is relevant to:

EU AI Act

Article 15-Accuracy, Robustness and Cybersecurity

ISO 42001

A.6.2.6-AI System Security Controls — Dependency Management

NIST AI RMF

GOVERN 6.1-Policies for Third-Party AI Risks

OWASP LLM Top 10

LLM03:2025-Supply Chain Vulnerabilities

Frequently Asked Questions

What is CVE-2025-25362?

Is CVE-2025-25362 actively exploited?

Proof-of-concept exploit code is publicly available for CVE-2025-25362, increasing the risk of exploitation.

How to fix CVE-2025-25362?

1. PATCH: Upgrade spacy-llm to 0.7.3 — the fix is available in the referenced commit (8bde049). No API changes required. 2. WORKAROUND (if patching is blocked): Treat the template field as untrusted input — strip or reject any Jinja2/template syntax characters ({%, {{, }}) before passing data to spacy-llm. 3. AUDIT: Search codebases for uses of spacy-llm's template parameter accepting request body data, file contents, or any externally-sourced strings. 4. DETECT: Monitor for anomalous process spawning from Python interpreters running spaCy — unexpected subprocesses, outbound connections from ML worker nodes, or unusual file access patterns. 5. CONTAIN: If exposure is confirmed, rotate all credentials accessible to the compromised process (LLM API keys, DB credentials, cloud IAM tokens).

What systems are affected by CVE-2025-25362?

This vulnerability affects the following AI/ML architecture patterns: NLP preprocessing pipelines, RAG ingestion pipelines, document processing APIs, LLM integration frameworks, agent frameworks with NLP components.

What is the CVSS score for CVE-2025-25362?

CVE-2025-25362 has a CVSS v3.1 base score of 9.8 (CRITICAL). The EPSS exploitation probability is 0.80%.

What is the AI security impact?

Affected AI Architectures

NLP preprocessing pipelinesRAG ingestion pipelinesdocument processing APIsLLM integration frameworksagent frameworks with NLP components

MITRE ATLAS Techniques

AML.T0010.001AI Software

AML.T0025Exfiltration via Cyber Means

AML.T0049Exploit Public-Facing Application

AML.T0050Command and Scripting Interpreter

Compliance Controls Affected

EU AI Act: Article 15

ISO 42001: A.6.2.6

NIST AI RMF: GOVERN 6.1

OWASP LLM Top 10: LLM03:2025

What are the technical details?

Original Advisory

A Server-Side Template Injection (SSTI) vulnerability in Spacy-LLM v0.7.2 allows attackers to execute arbitrary code via injecting a crafted payload into the template field.

Exploitation Scenario

An adversary targeting an organization running a document analysis API backed by spacy-llm submits a crafted document or API request with a payload such as {{ ''.__class__.__mro__[1].__subclasses__()[X].__init__.__globals__['os'].popen('id').read() }} injected into a field that is forwarded to spacy-llm's template processor. The template engine evaluates the payload server-side, executing arbitrary OS commands under the Python process identity. The attacker pivots to exfiltrate LLM API keys stored in environment variables, dumps model artifacts from disk, and establishes a reverse shell into the ML infrastructure — all without any valid credentials.

Weaknesses (CWE)

CWE-1336Improper Neutralization of Special Elements Used in a Template EnginePrimary CWE-94Improper Control of Generation of Code ('Code Injection')Primary

CWE-1336 — Improper Neutralization of Special Elements Used in a Template Engine: The product uses a template engine to insert or process externally-influenced input, but it does not neutralize or incorrectly neutralizes special elements or syntax that can be interpreted as template expressions or other code directives when processed by the engine.

[Architecture and Design] Choose a template engine that offers a sandbox or restricted mode, or at least limits the power of any available expressions, function calls, or commands.
[Implementation] Use the template engine's sandbox or restricted mode, if available.

Source: MITRE CWE corpus.