Case Study: Prompt Injection in LLM Chatbots - How a Jinja2 CVE Enables Reverse Shell Attacks

Nestria Threat Insights: This article is part of our ongoing research into securing GenAI driven systems.

As enterprises accelerate their adoption of generative AI, conversational chatbots and self-service agents are fast becoming the default interface between users and digital systems. But behind the ease of conversation and intelligent automation lies a growing risk: application-layer vulnerabilities hidden deep within the GenAI stack—including LLM chatbot risks that are not always visible during development.

In a recent investigation, the Nestria research team identified how a known vulnerability in the Python-based Jinja2 templating engine—commonly used with Flask, a popular framework for GenAI web apps—can be exploited to achieve remote code execution (RCE) and reverse shell access through a chatbot interface.

This is not a theoretical risk. In controlled test environments, we successfully demonstrated an end-to-end exploit chain triggered by a crafted user prompt, flowing through a Flask-based application into a vulnerable template rendering process. The result: full system-level access. While the technical specifics of the red team method remain confidential, our findings shed light on a wider class of vulnerabilities that current AI application architectures often overlook.

Research Insight: From Prompt to Shell

At the heart of this exploit is a common development pattern: Flask, widely used to build GenAI APIs and UI frontends, relies on Jinja2 to render HTML templates. In many chatbot implementations, especially those that log user inputs or dynamically render LLM responses, the boundary between safe and unsafe content becomes blurred.

This sets the stage for a high-risk interaction between:

• Open-ended, often unfiltered prompt inputs, and

• A template engine vulnerable to injection via unsanitized content.

One such overlooked threat is a critical vulnerability in Jinja2, tracked as CVE-2024-56326. The flaw allows arbitrary Python code execution via str.format() when attackers control the template content. Although it’s patched in jinja2>=3.1.5, many GenAI deployments still run older or unpinned versions—leaving them silently exposed. In GenAI applications, where LLM-generated outputs or user prompts frequently reach rendering logic, this becomes a viable entry point for full RCE and reverse shell attacks.

Our team demonstrated how LLM-assisted prompt crafting, when aimed at insecure Flask implementations, can exploit this pathway. In our testbed, the attack unfolded as follows:

• 🔸 Untrusted user prompt input

• → flows through Flask route handling

• → reaches Jinja2 template rendering

• → triggers the CVE and executes system commands

• → results in reverse shell access to the host machine

This kind of vulnerability surfaces when developers inadvertently expose template rendering to user-controlled data—common in GenAI apps that personalize outputs, log conversations, or generate dynamic UI components using LLM-generated content.

⚠️ We do not disclose red teaming methodology. Our intent is to raise awareness and promote proactive prevention.

Why This Is a GenAI-Specific Risk

While this vulnerability affects general Flask applications, LLM interfaces make it easier to reach due to:

• Input Complexity: GenAI models accept long, unstructured prompts - ideal for attacker-controlled payloads.

• Developer Practices: Flask + Jinja2 are often used for rapid GenAI app prototyping, with little input validation or sandbox enforcement.

• Agentic Workflows: LLMs connected to tools or APIs can unknowingly pass tainted inputs into templated instructions or views.

This confluence makes LLM input surfaces + templating vulnerabilities a new attack vector that traditional web security audits may overlook.

How Nestria Mitigates the Risk

Nestria’s security platform is purpose-built for AI-native systems, with deep integration into both application and inference layers. In this scenario, our solution:

1. Identifies vulnerable dependencies like Jinja2 in Flask-based AI UIs using our AI-BOM and AI component scanner.

2. Flags risk-prone input handling patterns where prompt content may flow into insecure templates or render calls.

3. Enforces runtime policies via our Inference Firewall and Threat Orchestrator to block unsafe tool invocations, command injection, or unauthorized shell calls.

4. Provides remediation guidance for developers to isolate, sanitize, or refactor code before exposure.

Our tools operate in both design-time (static scan) and runtime (inference monitoring + policy enforcement) modes, ensuring end-to-end protection across the AI deployment lifecycle.

Why This Matters for Security, IT and Business Leaders

This is more than a Python bug—this is about AI-era security hygiene. Left unpatched, this CVE allows:

• Infrastructure compromise (RCE & reverse shell via a chatbot prompt)

• Loss of trust in GenAI systems exposed to public or internal users

• Regulatory non-compliance under frameworks like EU AI Act, ISO/IEC 23894, MAS FEAT, and NIST AI RMF

• Exfiltration or tampering of sensitive data or models

In a world where chatbots are the first point of enterprise contact, this risk is no longer optional to fix.

Secure AI by Design, Not by Patch

At Nestria, we believe security must be embedded from design to deployment—especially in GenAI systems where dynamic logic, open inputs, and tool access converge.

If you're building GenAI applications or deploying chatbots across your enterprise, it's time to assess your exposure to:

• CVEs in AI application stacks

• Input injection and prompt hijacking

• Inference-time policy gaps

🔍 Request a free security scan of your GenAI environment - before attackers exploit what you don’t yet see.

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