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Inside the High Stakes Race to Secure Generative AI
Large Language Models (LLMs) have revolutionized how organizations operate, with 71% of companies now using generative AI regularly in at least one part of their business. However, this rapid adoption has introduced unprecedented security challenges that traditional cybersecurity frameworks weren't designed to address. As AI systems become integral to critical business operations, securing them has become a strategic imperative that extends far beyond conventional IT security.
Recent research reveals that 94% of organizations expose some of their model code to the internet, while 17% have unencrypted API keys providing access to AI services stored in their code repositories. These statistics underscore a critical reality: the race to deploy AI has often outpaced security considerations, creating vulnerabilities that sophisticated attackers are already beginning to exploit.
Unlike traditional software systems, LLMs operate on vast amounts of data and are fundamentally non-deterministic, making them unpredictable and challenging to secure. The core issue stems from the inability of current model architectures to distinguish between trusted developer instructions and untrusted user input. This architectural limitation creates attack vectors that simply don't exist in conventional applications.
LLM security involves practices and technologies that protect LLMs and their associated infrastructure from unauthorized access, misuse, and other security threats. This includes safeguarding the data they use, ensuring the integrity and confidentiality of their outputs, and preventing malicious exploitation throughout the entire AI lifecycle.
The Open Web Application Security Project (OWASP) has identified the most critical security risks facing LLM applications. Understanding these risks is essential for building robust AI security programs.
Prompt injection is a security risk where attackers manipulate the input prompts to an LLM to elicit undesirable or harmful responses. This vulnerability occurs when user prompts alter the LLM's behavior or output in unintended ways, potentially leading to unauthorized access, data breaches, and compromised decision-making.
Types of prompt injection include:
Mitigation strategies:
Failure to protect against disclosure of sensitive information in LLM outputs can result in legal consequences or a loss of competitive advantage. LLMs can inadvertently reveal personal data, proprietary information, or confidential business details through their responses.
Protection techniques:
LLM supply chains are susceptible to various vulnerabilities, which can affect the integrity of training data, models, and deployment platforms. The supply chain includes everything from third-party model weights and datasets to orchestration pipelines and open source dependencies.
Key risk areas:
Mitigation strategies:
Data poisoning refers to manipulation of pre-training data or data involved within the fine-tuning processes to introduce vulnerabilities, backdoors or biases that could compromise the model's security, effectiveness or ethical behavior. This attack can severely impact model performance and introduce persistent vulnerabilities that are extremely difficult to detect and remove.
Common attack vectors:
Defense mechanisms:
When organizations fail to scrutinize LLM outputs, any outputs generated by malicious users could cause problems with downstream systems. Improper output handling can result in cross-site scripting (XSS), cross-site request forgery (CSRF), server-side request forgery (SSRF), remote code execution (RCE), and other types of attacks.
Prevention approaches:
Granting LLMs unchecked autonomy to take action can lead to unintended consequences, jeopardizing reliability, privacy, and trust. This becomes particularly concerning as we enter the era of agentic AI systems that can perform actions across multiple systems.
Prevention approaches:
LLM system prompts can leak sensitive information. While designed to guide the model, they may unintentionally expose secrets, aiding further attacks. System prompts should not be considered secrets or used for security control; sensitive data like credentials or connection strings should not be stored within them. If a prompt discloses roles, permissions, or sensitive data, the core risk isn't the disclosure itself, but the application's weak session management and authorization, and improper sensitive data storage.
Prevention approaches:
This vulnerability arises from weaknesses in the generation, storage, or retrieval of vectors and embeddings, particularly in Retrieval Augmented Generation (RAG) systems. Exploiting these weaknesses can lead to harmful content injection, manipulated model outputs, or unauthorized access to sensitive information.
Prevention approaches:
LLM misinformation, where models produce believable but false information, is a major vulnerability, causing security risks, reputational damage, and legal issues. A primary cause is hallucination, where LLMs generate fabricated content by filling training data gaps with statistical patterns, leading to unfounded answers. Other factors include training data biases and incomplete information.
Prevention approaches:
Unbounded Consumption in LLMs allows users to make excessive, uncontrolled inferences, risking denial of service, financial losses, model theft, and service degradation due to high computational demands.
Prevention approaches:
AI red teaming is the practice of stress-testing AI systems by simulating real-world adversarial attacks to uncover vulnerabilities. This proactive approach helps identify weaknesses before malicious actors can exploit them.
Key components of AI red teaming:
Implementation strategies:
AI systems face dynamic threats that require continuous monitoring to prevent security breaches and service disruptions. Organizations need specialized incident response capabilities for AI-specific security events.
Monitoring strategies:
Incident response planning:
Unlike traditional software systems, LLMs belong to a rapidly evolving field where attackers and defenders are in a constant race. Comprehensive testing approaches are essential:
Testing methodologies:
The regulatory environment for AI is rapidly evolving, with AI regulations becoming stricter, especially for high-risk applications. Organizations must prepare for increasing compliance requirements:
Key regulatory frameworks:
Compliance strategies:
The LLM supply chain includes everything from third-party model weights and datasets to orchestration pipelines and open source dependencies. As AI systems become more interconnected, supply chain security becomes increasingly critical:
Emerging risks:
Advanced protection measures:
As AI systems become more autonomous and capable, security challenges will continue to evolve. Organizations must prepare for:
Emerging capabilities:
Evolving defense strategies:
As organizations continue to integrate LLMs into critical business processes, security cannot be an afterthought. The unique characteristics of AI systems—their non-deterministic nature, vast data requirements, and complex supply chains—demand specialized security approaches that go beyond traditional cybersecurity measures.
Key takeaways for organizations:
The future of AI security will require continuous adaptation and innovation. Organizations that invest in comprehensive LLM security programs today will be better positioned to leverage AI's transformative potential while protecting against evolving threats. As the AI landscape continues to mature, security must remain at the forefront of every deployment decision.
The stakes are too high, and the potential consequences too severe, to treat LLM security as anything less than a strategic business imperative. By implementing the frameworks, practices, and technologies outlined in this guide, organizations can build resilient AI systems that deliver value while maintaining the trust and security that stakeholders demand.
Prompt injection, data leakage, and poisoned models are huge enterprise risks. we45 helps organizations implement OWASP Top 10 safeguards, run LLM security assessments, and build trusted AI applications from the ground up. Talk to our experts and secure your AI stack before attackers do
Large language models (LLMs) are artificial intelligence systems capable of generating human-like text by processing vast amounts of data. Their security is crucial because LLMs now power core business functions, and vulnerabilities can lead to data breaches, misinformation, and unauthorized access, impacting both organizations and stakeholders.
In 2025, over 71% of companies report using generative AI in at least one business function, illustrating widespread adoption across industries.
LLMs are uniquely vulnerable because they handle unpredictable, non-deterministic data patterns, making it difficult to distinguish trusted instructions from untrusted input. This non-determinism creates attack vectors like prompt injection that are not present in traditional IT systems.
Prompt injection is when attackers manipulate the input prompts to alter LLM behavior or output, potentially causing data leakage or breaches. Mitigation includes robust input validation, content filtering, strict privilege control, and human-in-the-loop decision processes.
The OWASP Top 10 for LLMs are: Prompt Injection, Sensitive Information Disclosure, Supply Chain Vulnerabilities, Data and Model Poisoning, Improper Output Handling, Excessive Agency, System Prompt Leakage, Vector and Embedding Weaknesses, Misinformation, Unbounded Consumption.
LLM supply chains involve third-party datasets, model weights, plugins, and infrastructure, any of which can be compromised to introduce malicious code, tainted training data, or backdoors. Security assessments, provenance tracking, and audits are essential to mitigate these risks.
Data and model poisoning involves attackers introducing malicious or manipulated data during the model’s training or fine-tuning phases, causing persistent vulnerabilities, bias, or inappropriate outputs that are difficult to detect and remediate.
System prompt leakage occurs when sensitive information in prompts is exposed to users or attackers. Best practices include removing critical data from prompts, enforcing strict access control, auditing prompts, and monitoring for potential information exposure.
Weaknesses in how vectors and embeddings are generated or retrieved, especially in retrieval-augmented generation setups, can allow attackers to insert harmful content or gain unauthorized access. Encryption, robust validation, and secure retrieval processes are critical protections.
Organizations should implement fact-checking for outputs, diversify training data, use adversarial training methods, and involve human reviewers for critical decisions to counteract LLM misinformation and hallucination.
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