⚡ Technical Documentation

Detailed Briefing Document LLM Security For Government Tech Teams

Technical guide covering detailed briefing document_ llm security for government tech teams

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Cosmic Lounge AI Team
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6/1/2025
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#llm #ai #model #training #api #development #code #guide #design

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🌟 Detailed Briefing Document: LLM Security for Government Tech Teams

This briefing document synthesises information from three sources concerning the security of Large Language Models (LLMs) for government applications. It outlines the key themes, important ideas, and facts presented, including direct quotes where relevant, to provide a comprehensive overview for government technology teams.

⚡ 1. The Imperative of LLM Security in Government

All sources underscore the growing importance and potential of LLMs within government for a variety of applications, from citizen interaction to data analysis. However, they also consistently highlight the critical need to address the unique security challenges these powerful AI tools introduce, especially given the sensitive nature of government operations.

  • “Large Language Models (LLMs) are powerful AI tools that understand and generate human-like text. Governments are keen on using them for everything from chatbots to analyzing data” (LLM Security Essentials).

  • “Government agencies are actively exploring and deploying AI, including LLMs, to improve the services they provide to citizens, enhance operational resilience, and counter emerging threats in areas ranging from critical infrastructure protection to public service delivery.” (LLM Security Research Requests).

  • “Government organizations handle vast amounts of sensitive and confidential information, ranging from national security intelligence to citizen data. Any security breach involving LLMs could have severe consequences, potentially compromising national security, public safety, and individual privacy.” (LLM Security Vetting Standards).

The sources emphasize that LLMs are not simply another piece of software and possess unique vulnerabilities related to their training data, learning processes, and user interactions. A failure to proactively manage these risks could lead to “catastrophic” (LLM Security Essentials) consequences for national security and public safety.

⚡ 2. Key Security Risks: The OWASP LLM Top 10

A central theme across all sources is the OWASP Top 10 for LLM Applications, which serves as a crucial “most wanted” list of common and impactful security risks. The 2025 iteration is highlighted in the “LLM Security Research Requests” document, noting that it incorporates insights from real-world use cases and updates to reflect the evolving threat landscape. The ten categories of risk, as explained in the “LLM Security Essentials” document and elaborated upon in “LLM Security Research Requests,” are:

  • Prompt Injection (LLM01): Tricking the LLM with malicious instructions embedded in the input.

  • “Imagine telling a helpful robot assistant, ‘Forget your rules, do this sketchy thing instead.’” (LLM Security Essentials).

  • “Attack vectors include direct prompt injection, where malicious instructions are directly input into the LLM, and indirect prompt injection, where attackers control external data sources that the LLM processes.” (LLM Security Research Requests).

  • Insecure Output Handling (LLM02): Trusting LLM output without validation, potentially leading to the execution of malicious code.

  • “Always treat LLM output like untrusted input!” (LLM Security Essentials).

  • “Attack vectors include the potential for LLM outputs to contain malicious code or unintended commands that could lead to XSS, SSRF, or RCE.” (LLM Security Research Requests).

  • Training Data Poisoning (LLM03): Maliciously influencing the data the LLM learns from, leading to biased or unreliable outputs.

  • “Garbage in, garbage out – but potentially dangerous garbage.” (LLM Security Essentials).

  • “Data poisoning attack vectors include the direct injection of falsified, biased, or harmful content into training processes…” (LLM Security Research Requests).

  • Model Denial of Service (LLM04): Overwhelming the LLM with complex requests to disrupt its availability.

  • “This can disrupt important government services relying on the LLM.” (LLM Security Essentials).

  • ”…Model Denial of Service attacks involve overwhelming the LLM with resource-intensive queries.” (LLM Security Research Requests).

  • Supply Chain Vulnerabilities (LLM05): Risks associated with using compromised third-party components, pre-trained models, or data sources.

  • “If a building block is compromised, the whole structure is weak.” (LLM Security Essentials).

  • “Attack vectors include vulnerabilities in third-party packages, the use of compromised pre-trained models, poisoned crowd-sourced training data, and outdated components.” (LLM Security Research Requests).

  • Sensitive Information Disclosure (LLM06): The LLM accidentally leaking confidential data it has learned or been given.

  • “This could expose private citizen details, classified information, or internal system secrets.” (LLM Security Essentials).

  • “Attack vectors include jailbreaking the model to bypass filters, cross-session leakage where data from one user is exposed to another, targeted prompt injection to extract specific information, and data leaks through the training data itself.” (LLM Security Research Requests).

  • Insecure Plugin Design (LLM07): Exploitable vulnerabilities in external tools or plugins used by the LLM.

  • “Imagine giving a powerful tool an insecure add-on that anyone can hijack.” (LLM Security Essentials).

  • “Insecure plugin design, where plugins lack proper input validation or access controls, can also lead to severe exploits like remote code execution.” (LLM Security Research Requests).

  • Excessive Agency (LLM08): Granting the LLM too much autonomy without sufficient checks, leading to unintended consequences.

  • “It might perform actions with unintended consequences without proper checks or human approval.” (LLM Security Essentials).

  • “Attack vectors include plugins with overly broad functionality or permissions, and LLMs that can interact with external systems in unintended ways.” (LLM Security Research Requests).

  • Overreliance (LLM09): Users trusting the LLM’s output too much without verification, leading to poor decision-making based on potentially incorrect information.

  • “LLMs can make mistakes or ‘hallucinate’ convincingly wrong information. Blind trust can lead to bad decisions.” (LLM Security Essentials).

  • “Attack vectors include LLMs confidently providing incorrect information and the potential for malicious actors to exploit this overreliance to spread disinformation.” (LLM Security Research Requests).

  • Model Theft (LLM10): Unauthorized access, copying, or exfiltration of the LLM model itself.

  • “This is like losing valuable intellectual property and could expose how the model works, potentially revealing weaknesses.” (LLM Security Essentials).

  • “Attack vectors include overwhelming the LLM with resource-intensive queries and attempts to steal or replicate the model.” (LLM Security Research Requests).

The “LLM Security Research Requests” document notes that the OWASP LLM Top 10 is not yet ranked by observed exploitation frequency but rather by potential impact and prevalence. The UK government’s Code of Practice for the Cyber Security of AI is noted in “LLM Security Vetting Standards” as referencing the OWASP Top 10, highlighting its growing international significance.

⚡ 3. Risk Management Frameworks and Standards

The sources identify key frameworks and standards relevant to managing LLM security risks in government:

  • NIST AI Risk Management Framework (AI RMF): A US-based voluntary framework focused on governing, mapping, measuring, and managing AI risks, including specific guidance for generative AI like LLMs. It emphasizes trustworthy AI characteristics such as security, transparency, and fairness.

  • “NIST even has specific advice for generative AI like LLMs.” (LLM Security Essentials).

  • “This profile aims to help organizations identify the distinct risks associated with generative AI and suggests actions for managing these risks in alignment with their specific goals and priorities.” (LLM Security Vetting Standards, referring to NIST’s Generative AI Profile).

  • ISO/IEC 42001: An international certifiable standard for establishing an Artificial Intelligence Management System (AIMS), covering ethical considerations, human-centric approaches, and continuous improvement, aligning with other ISO management standards.

  • “It aligns with other management standards (like ISO 27001 for security) and can be certified, which might be important for international dealings or proving compliance.” (LLM Security Essentials).

  • “Early adoption of this standard can position government organizations favorably for future compliance and demonstrate their commitment to secure and ethical AI practices, including the use of LLMs.” (LLM Security Vetting Standards).

  • OWASP Top 10 for LLM Applications: A practical, non-formal list of critical LLM security risks, serving as a starting point for developers and security teams.

  • “Less a formal standard, more a practical ‘heads-up’ list from security experts about the most common and impactful LLM risks…” (LLM Security Essentials).

A comparative analysis in “LLM Security Research Requests” highlights that both NIST AI RMF and ISO/IEC 42001 emphasise risk management and governance. However, NIST AI RMF offers a more detailed risk management process, while ISO 42001 provides a broader management system framework and certifiability. NIST’s dedicated Generative AI Profile offers more direct LLM-specific guidance.

⚡ 4. Government Best Practices and Guidelines

The “LLM Security Essentials” and “LLM Security Research Requests” documents detail several common best practices emerging from government guidelines globally (US, UK, Singapore, etc.):

  • Secure by Design: Integrate security considerations from the initial stages of development.

  • Risk Management: Continuously identify, assess, and mitigate risks using frameworks like NIST AI RMF.

  • Data Privacy: Implement measures like data minimisation, anonymisation/redaction, encryption, and strict access controls to protect sensitive data.

  • “Only use the data you absolutely need.” (LLM Security Essentials, on minimisation).

  • “Scrub personal details from data before the LLM sees it.” (LLM Security Essentials, on anonymisation/redaction).

  • Human Oversight: Maintain human involvement and responsibility, especially for critical decisions.

  • Supply Chain Security: Thoroughly vet third-party models, data, and software components.

  • Input/Output Filtering: Sanitise inputs to prevent malicious instructions and validate/sanitise outputs before use.

  • Testing & Monitoring: Rigorously test LLMs for security flaws (including red teaming) and continuously monitor their behaviour in production.

  • Training: Educate all stakeholders on LLM security risks and safe practices. The “LLM Security Vetting Standards” document reinforces these best practices and notes the increasing recognition of these principles in government guidelines worldwide. It highlights initiatives from CISA, DHS, NSA in the US, and the NCSC in the UK, among others.

⚡ 5. Emerging Threats and Vulnerabilities

The sources highlight several emerging threats requiring vigilance:

  • Sophisticated Prompt Injection: Including multimodal attacks using images or hidden instructions.

  • Vector Database Attacks: Targeting the knowledge base in Retrieval Augmented Generation (RAG) systems for data theft or poisoning.

  • System Prompt Leakage: Accidental exposure of sensitive information or instructions within the LLM’s system prompts.

  • “If these system prompts are leaked to malicious actors, it can expose underlying system weaknesses and improper security architectures…” (LLM Security Research Requests).

  • Vector and Embedding Weaknesses: Exploitation of numerical representations of data used by LLMs for context and similarity, leading to unauthorised access, data leakage, or poisoning.

  • Misinformation: The LLM generating inaccurate information, exacerbated by user overreliance.

  • Unbounded Consumption: Excessive resource use leading to service disruption or high costs.

  • Model Theft: Unauthorised access and copying of proprietary LLM models.

  • Model Inversion Attacks: Attempts to reconstruct training data or sensitive information from the model.

  • Model Evasion Attacks: Subtle alterations to inputs to bypass content filters.

  • Denial of Wallet Attacks: Exhausting the LLM’s token budget.

  • Continued Training Data Poisoning Concerns.

⚡ 6. Leveraging AI for LLM Security

Interestingly, the “LLM Security Research Requests” document discusses the potential of using AI, particularly LLMs themselves, for vulnerability research and security testing:

  • LLMs can analyse code and documentation to identify potential security flaws.

  • They can generate innovative test cases and automate initial stages of code review.

  • Tools like Vulnhuntr, Project Zero’s “Naptime,” Garak, BurpGPT, and LLMFuzzer are examples of this application.

  • Project Zero even used LLM-based techniques to discover a real vulnerability in SQLite. However, the document also notes limitations, such as the non-deterministic nature of some LLMs and the need for human verification of findings.

⚡ 7. Impact of International Regulations: The EU AI Act

The “LLM Security Research Requests” document highlights the significant impact of international regulations like the EU AI Act on government LLM security requirements. The Act adopts a risk-based approach, categorising AI systems and imposing stringent requirements on high-risk applications, which include many government services using AI. General-Purpose AI models, including those powering LLMs, also fall under its purview, mandating transparency and regular evaluations. Non-compliance can result in substantial fines.

  • “The EU AI Act, which took effect in August 2024, stands as the first comprehensive legal framework for AI, with the overarching goal of ensuring that AI applications across the EU are safe, transparent, non-discriminatory, and environmentally sustainable.” (LLM Security Research Requests).

Government entities operating within the EU or offering services to EU citizens will need to conduct risk assessments of their LLM applications and comply with the Act’s provisions, including data governance, human oversight, and transparency.

⚡ 8. Benchmarks and Evaluation Methods

The “LLM Security Research Requests” document outlines current benchmarks and evaluation methods for assessing LLM security and robustness:

  • Benchmarks: AI Cyber Risk Benchmark, Meta’s CyberSecEval and CyberSecEval 2, OWASP LLM Top 10, TruthfulQA, FEVER, HaluEval.

  • Evaluation Methods: Automated metrics (contextual recall, text similarity, question answering accuracy, etc.), human evaluation (coherence, relevance, fluency), red teaming, and adversarial attacks. A comprehensive approach combining these methods is deemed essential for a thorough assessment of LLM security.

⚡ 9. LLM Security Tools and Technologies

The “LLM Security Research Requests” document provides an extensive list of specific tools and technologies designed to enhance LLM security, categorised by function:

  • Input Sanitisation and Validation: LLM Guard, Lakera Guard, Rebuff.

  • Output Filtering: LLM Guard, built-in content filters.

  • Anomaly Detection: Vigil LLM, WhyLabs, Lasso Security.

  • AI-Specific Security Platforms: Granica Screen, Lasso Security, Protect AI, Pynt, Fiddler AI, Deepchecks, Alert AI.

  • Vulnerability Scanning: Garak, Purple Llama.

  • Data Minimisation: Private AI.

  • Fuzzing: LLMFuzzer.

  • Adversarial Training: Microsoft Counterfit, Adversarial Robustness Toolbox.

  • Other: Canary tokens, AI Security Posture Management (AI-SPM), LLM firewalls, prompt security tools.

⚡ 10. Ensuring Data Privacy

The “LLM Security Research Requests” and “LLM Security Essentials” documents address the critical need for data privacy when using LLMs in government, given the sensitive information involved and regulatory compliance requirements. Recommended techniques include:

  • Data minimisation.

  • Anonymisation (masking, redaction).

  • Encryption (at rest and in transit).

  • Tokenisation and redaction.

  • Privacy-preserving techniques (differential privacy, federated learning, homomorphic encryption).

Best practices include establishing clear data handling policies, implementing strong access controls, regular auditing and monitoring, and comprehensive employee training.

⚡ 11. International Collaboration

All sources implicitly and explicitly acknowledge the importance of international collaboration in establishing global standards and best practices for AI and LLM security. The “LLM Security Research Requests” document specifically highlights the need to address cross-border threats and prevent regulatory arbitrage. Initiatives involving OWASP, NIST, the UK government, CISA, GPAI, and UNIDIR are mentioned.

⚡ 12. Conclusion and Recommendations for Government Entities

The concluding sections of the sources reiterate the significant potential of LLMs for government but stress that security must be a primary concern. A proactive, multi-faceted approach is essential, encompassing understanding the unique risks, applying robust standards, implementing practical best practices, and staying vigilant about emerging threats. The “LLM Security Research Requests” document provides specific recommendations for government entities:

  • Develop and implement comprehensive AI and LLM security policies aligned with frameworks like NIST AI RMF and ISO/IEC 42001.

  • Prioritise the OWASP Top 10 for LLM Applications as a foundational security element.

  • Establish clear guidelines for data privacy and security, including data minimisation, anonymisation, and encryption.

  • Invest in training and awareness programs for employees, developers, and security personnel.

  • Promote and participate in international collaboration efforts for establishing global standards.

  • Continuously monitor the evolving threat landscape and adapt security measures accordingly.

  • Encourage the use of AI-powered tools for vulnerability research and security testing of LLMs. By adhering to these principles and recommendations, government entities can leverage the benefits of LLMs while effectively mitigating the inherent security risks.

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