Securing LLMs in Banking: Navigating Risks and Implementing Solutions

17 January 2024

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In the fast-evolving landscape of the banking industry, the integration of cutting-edge technologies like Large Language Models (LLMs) offers unparalleled opportunities for enhanced customer interactions and operational efficiency. However, as financial institutions embrace these advancements, it becomes imperative to understand and address the unique security and fraud risks associated with LLMs, particularly within the context of the banking sector.

It’s possible to jailbreak LLMs like ChatGPT 92% of the time.


The more advanced the model, the more susceptible to persuasive advanced prompts (PAPs).

Adversarial Prompt Attacks: Safeguarding Financial Conversations

In the realm of banking, the risks associated with adversarial prompt attacks on LLMs take on a heightened significance. Banking conversations often involve sensitive information, making it crucial to categorize and defend against adversarial prompts effectively. Classifying adversarial prompts becomes vital as banking interactions demand a higher level of precision and security. Understanding and mitigating categories like pretending, attention shifting, and privilege escalation are essential.

Categorizing the Prompts: An Empirical Study

  • Pretending: These prompts cleverly alter the conversation’s background or context while preserving the original intention. For instance, by immersing ChatGPT in a role-playing game, the context shifts from a straightforward Q&A to a game environment. Throughout this interaction, the model recognizes that it’s answering within the game’s framework.
  • Attention Shifting: A more nuanced approach, these prompts modify the conversation’s context and intention. Some examples include prompts that require logical reasoning and translation, which can potentially lead to exploitable outputs.
  • Privilege Escalation: This category is more direct in its approach. Instead of subtly bypassing restrictions, these prompts challenge them head-on. The goal is straightforward: elevate the user’s privilege level to ask and receive answers to prohibited questions directly. This strategy can be utilized in prompts asking ChatGPT to enable “developer mode.”

Jailbreaking: Preserving the Integrity of Financial Advice

In the banking industry, the integrity of financial advice and information is of utmost importance. The vulnerability of LLMs to “jailbreaking” directly threatens the trustworthiness of the information they provide.

Examples of Jailbreaking in Banking: Instances where users manipulate LLMs to endorse questionable financial strategies or provide misleading information, underscore the need for robust safeguards.

Assisting Scamming and Phishing: Fortifying Financial Conversations

The integration of LLMs into banking operations brings the risk of indirect prompt injection, potentially leading to scams and phishing attacks targeting customers and their financial information. With financial data at stake, guarding against prompt injection attacks is paramount. Banks must ensure that LLMs generate responses aligned with regulatory compliance and ethical guidelines.

Examples of Indirect Prompt Injection in Banking: Malicious actors leverage hidden prompts to manipulate LLMs into generating misleading financial advice or attempting to extract sensitive customer data, which represents a serious threat.

Data Poisoning: Preserving Financial Integrity from the Start

For financial institutions, the threat of data poisoning poses a risk to the very foundation of LLMs. The accuracy and integrity of financial information depend on the purity of training data.

Economic Incentive for Attackers in Finance: The financial sector’s attractiveness as a target for data poisoning attacks highlights the need for proactive measures to ensure untampered training data for LLMs.

Examples of Indirect Prompt Injection in Banking: Malicious actors leverage hidden prompts to manipulate LLMs into generating misleading financial advice or attempting to extract sensitive customer data, which represents a serious threat.

Defense Tactics: Crafting a Security Framework for Financial Conversations

In the banking industry, implementing defense tactics against adversarial attacks is not just a choice but a regulatory necessity.

  • Adding Defense in the Instruction: Crafting instructions that emphasize the importance of regulatory compliance and ethical behavior is crucial to guide LLMs in providing accurate and secure financial information.
  • Parameterizing Prompt Components: Drawing inspiration from the finance industry’s robust security measures, parameterizing prompt components can create a layered defense, separating instructions from inputs for enhanced security.
  • Quotes and Additional Formatting: Utilizing quotes and additional formatting techniques can fortify financial conversations, making it more challenging for attackers to manipulate LLM outputs in a banking context.

Implications and Conclusion: The Future of Secure Financial Conversations

As the banking industry continues to integrate LLMs into customer interactions and operational processes, the implications of securing these language models cannot be overstated. Striking a delicate balance between innovation and security is a non-negotiable imperative in ensuring the responsible and secure growth of AI technologies in the financial sector. By proactively addressing the unique security challenges posed by LLMs, financial institutions can pave the way for a future where AI-driven conversations within the banking industry are not only technologically advanced but also resilient to adversarial threats, ultimately fostering trust and confidence among customers and stakeholders alike.

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