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As organizations increasingly adopt Large Language Models (LLMs) and GenAI, safeguarding data and knowledge becomes an even greater priority. According to a recent OWASP report, sensitive data has climbed to the second most pressing concern for security administrators.
Sensitive information can affect both the LLM and its application context. This includes personal identifiable information (PII), financial details, health records, confidential business data, security credentials, and legal documents. Proprietary models may also have unique training methods and source code considered sensitive, especially in closed o
Separate sensitive data: Keep credentials, connection strings, and internal rules out of system prompts. Use secure vaults and external systems for managing these elements.
Another addition to OWASP’s updated list, this vulnerability underscores a fundamental issue: system prompts often act as both behavior guides and inadvertent repositories for sensitive information. When these prompts leak, the risks extend far beyond the disclosure of their content, exposing underlying system weaknesses and improper security architectures.
System prompts are essential for steering LLM behavior. They define how an application responds, filters content, and implements rules. But when they include sensitive data (API keys, internal user roles, or operational limits) they create a hidden liability. Worse, even without explicit disclosure, attackers can reverse-engineer prompts by observing model behavior and responses during interactions.
This risk isn’t just hypothetical; it could be the springboard for sophisticated exploits in 2025, including unauthorized access through extracted credentials, privilege escalation and security guardrail bypasses.
To stay ahead of the curve, organizations should adopt these best practices:
The shift toward domain-specific LLM agents is already underway, and this trend is expected to accelerate. Gartner predicts that by 2027, half of GenAI models that enterprises use will be designed for specific industries or business functions.
As these models become more available, we expect their adoption to ramp up through 2025. And it’s not just about productivity: security-minded leaders will find plenty to like in these smaller, more manageable models:
Unlike general-purpose LLMs hosted by third parties, smaller, specialized agents are often deployed on-premises or in private clouds. This allows organizations to maintain full control over their data flow. Their narrow focus enables strict access controls, compliance with industry regulations, and adherence to standards like HIPAA or GDPR.
By narrowing their scope, these models also reduce the attack surface, making them less vulnerable to exploits compared to widely accessible general-purpose models.
However, domain-specific LLM agents are not inherently more secure. Smaller organizations may lack the expertise and resources to implement robust security measures, leaving them more vulnerable to attacks like adversarial threats, data poisoning, or prompt injections. Additionally, their specialized focus could make them more predictable, allowing attackers with domain knowledge to craft targeted exploits.
For models of any size to meet enterprise expectations, speed is non-negotiable, especially as users demand real-time interactions. Lasso’s RapidClassifier is a patent-pending technology that minimizes delays while maintaining robust security. With RapidClassifier, we enable custom security policies to run in under 50 milliseconds, ensuring protection that matches the speed of Generative AI applications.
We’ve created a solution that bridges the gap between detection and prevention, offering real-time protection against potential threats. By addressing latency concerns head-on, we empower businesses to confidently unlock the full potential of LLMs without compromising on performance or user experience.
Attacks on Retrieval-Augmented Generation (RAG) pipelines have been optimized to boost the ranking of malicious documents during the retrieval phase. One study shows that most attacks settle around a 40% success rate, which can rise to 60% if you consider ambiguous answers as successful attacks.
Experts have seen this coming for some time, which is why Vector and Embedding Weaknesses have made it into the updated OWASP Top 10 for LLMs.
Unlike traditional methods that rely solely on static permissions (e.g., user roles or metadata), Context-Based Access Control (CBAC) evaluates the context of both the request and the response. This includes:
This enables dynamic access enforcement, putting the right data in front of the right people, and blocking sensitive or out-of-scope information when necessary. This level of granular control makes it possible to avoid the twin extremes of over-restriction and unintentional exposure. Organizations will need this granularity to manage mounting RAG risks through 2025 and beyond.
AI compliance will evolve from being a "nice-to-have" to a cornerstone of organizational compliance transformation strategies. Governments and industries worldwide are racing to establish secure and ethical AI practices. Key regulations such as the US Government’s National Security Memorandum (NSM), the EU AI Act, and other global initiatives are driving this shift.
Organizations adopting LLMs will seek to prioritize building secure and monitored environments, especially in sectors where data integrity and privacy are mission-critical such as healthcare and financial services, will prioritize building secure, monitored environments for GenAI. AI compliance will require dynamic, always-on risk management and adherence to evolving frameworks, much like GDPR did a decade ago. Companies that fail to prioritize AI compliance will face significant financial, legal, and reputational risks
As LLMs become ubiquitous in industries ranging from healthcare to finance, their security challenges will grow proportionally. The spotlight on emerging vulnerabilities in 2025 is a wake-up call: organizations must shift from reactive defenses to proactive, layered security strategies. By addressing the deeper issues of architecture and access control, the industry can build LLM applications that are as secure as they are transformative.
Offensive AI: Cybercriminals are using AI and LLMs to automate attacks, exploit vulnerabilities faster, and evade detection through advanced techniques like AI-generated phishing, adaptive malware, and large-scale prompt injection attacks on LLMs.
Defensive AI: On the other side of the corral, organizations and cybersecurity providers are deploying GenAI to analyze behavior patterns, identify anomalies, and respond to threats in real-time. The speed, adaptability, and sophistication of defensive AI solutions are critical to staying ahead of attackers.
As Palo Alto Networks notes, this race is becoming more complex, and traditional cybersecurity tools alone cannot keep up. Instead, the focus will shift toward AI-driven defense systems capable of learning and adapting alongside evolving threats.
In this arms race, Lasso Security’s advanced solutions can serve as a frontline defense against AI-driven threats targeting LLMs. Key capabilities include:
As LLM security enters a new phase, proactive solutions like RapidClassifier and AI-driven defenses are essential to staying ahead of emerging threats. Whether you're navigating system vulnerabilities, enhancing compliance, or mitigating latency issues, our team is here to help. Contact us today to learn how Lasso can safeguard your GenAI deployments while maximizing performance.
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