Strengthening LLM Security from the Get-Go: Autonomous Red Teaming in Action

Some call it “AI securing AI,” others say “AI for AI security,” or simply “AI security.” Regardless of the name, one thing is clear: AI is playing a growing role in addressing the security challenges posed by Large Language Models (LLMs) and other generative AI systems.

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But it’s not quite as simple as one AI application policing another. Enterprise leaders bear the responsibility of selecting the right tools to manage the security posture of the specific AI models they have deployed.

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Despite the growing reliance on LLMs, many organizations lack proper benchmarks or clear ownership when it comes to securing them. This gap leaves enterprises navigating uncharted territory in LLM security, often without the tools or frameworks they need to stay ahead of an ever-expanding list of threats.

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Lasso’s new autonomous red teaming feature fills this critical gap, empowering organizations to stress-test their AI models, identify vulnerabilities, and enhance security resilience—before attackers do.

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The OWASP Top 10 for LLMs Has Evolved:

Security Needs to Keep Up

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Since its initial release, OWASP’s Top 10 for LLMs has had to adapt to changes in the way security risks are playing out in the real world.

These shifts underscore the importance of proactively red-teaming to secure LLMs:

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Excessive Agency

This has taken on renewed significance now that agentic AI has begun to take center stage. These AI agents offer unprecedented autonomy in decision-making and even execution, but that comes with heightened security risks. The updated list emphasizes these risks and the need to get ahead of agentic risk.

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System Prompt Leakage

Data leaks were last year’s headache, but today’s attackers are after the hidden logic that powers AI applications. Exposing system prompts gives bad actors a roadmap to manipulate AI behavior, bypass safeguards, and even extract proprietary business intelligence. With LLM-driven apps increasingly handling sensitive tasks, securing system prompts is the new battleground for AI security.

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Vector and Embedding Weaknesses

Retrieval-Augmented Generation (RAG) is gaining traction because it enhances LLM applications by dynamically fetching external knowledge, improving accuracy, and ensuring responses remain up-to-date. However, this also introduces new security risks: attackers can manipulate retrieval sources to inject false or biased information, compromise internal data pipelines, or expose sensitive proprietary knowledge through unintended responses. Since RAG expands the attack surface by relying on external inputs, organizations must enforce strict access controls, validate data integrity, and continuously monitor retrieval processes to prevent exploitation.

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Traditional Red Teaming Can’t Keep Up With the Pace of GenAI Development

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Specialized human expertise is the bedrock of conventional red teaming. This includes:

• Simulating attacks to find weak points.
• Diagnosing the nature of these vulnerabilities.
• Analyzing system weaknesses for remediation.

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These teams rely on highly skilled personnel, and the workflow is resource-intensive, with each step adding more complexity and cost. Red teaming involves planning, conducting reconnaissance, exploiting vulnerabilities, and finally reporting and debriefing.

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For traditional IT systems, this model of red teaming can deliver a satisfactory return on investment by preventing breaches and downtime later on. But as they do in so many other respects, AI models are complicating this picture, and rendering older frameworks obsolete.

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Limits to Scale

Conventional red teaming may not be able to scale effectively in environments where AI and LLM applications are deployed across diverse and distributed systems. These applications often operate in multi-cloud setups or serve global user bases, each presenting unique attack vectors that are difficult to test comprehensively. 

Frequent updates to AI models add even more complexity, with rapid iteration cycles that generate new risks that red teaming isn’t designed to address. This lack of scalability results in partial coverage, leaving certain vulnerabilities unaddressed and creating blind spots in overall security.

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Playing Catch-Up with Diverse Models and Applications

AI and LLM applications are highly diverse, with unique architectures, datasets, and use cases that require specialized approaches to uncover vulnerabilities. Traditional red teaming methods often rely on generalized attack scenarios, which may fail to expose threats specific to these systems, such as adversarial inputs, data poisoning, or misuse of generated outputs. 

The lack of standardized frameworks for AI-specific testing makes it challenging to adapt red teaming efforts to different models and deployments. This inability to tailor attacks reduces the effectiveness of identifying critical, context-specific security risks.

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Blindness to Ethical Issues

Red teaming as we know it focuses on security vulnerabilities. But AI models also need to be tested for ethical considerations, such as bias and fairness. Ensuring AI decisions are unbiased and ethically sound requires a totally different set of priorities.

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Reimagining Red Teaming for an AI-Driven World

Lasso’s autonomous, automated red teaming feature is purposefully designed to be both model-agnostic and application-agnostic. It integrates with a variety of LLMs, eliminating compatibility barriers, and providing a comprehensive solution for enterprises relying on diverse AI applications.

1. Automated, Real-World Attack Simulation
   ‍Lasso’s system replicates the tactics of real-world adversaries, enabling organizations to anticipate and defend against evolving threats without manual intervention.
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2. Custom Guardrails for Ethical Compliance
   Built-in controls ensure every red teaming scenario aligns with ethical standards and regulatory requirements, striking the right balance between security and compliance.
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3. Proactive Remediation
   Beyond detection, the feature provides actionable insights, security best practices, identified gaps, and tailored recommendations for teams to fortify their systems.
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4. Agent Interactions for Deeper Analysis
   Using patent-pending offensive agents, the platform simulates realistic user behaviors to uncover vulnerabilities that surface only during dynamic interactions, such as misaligned responses or susceptibility to data leakage.
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5. Unlimited Scale and Creative Adaptability
   With the capacity to run limitless, up-to-date attack scenarios, Lasso’s red teaming ensures your defenses are tested against the latest and most creative adversarial tactics.

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This combination of technical sophistication and practical application sets Lasso’s automated red teaming apart, offering enterprises a comprehensive and future-proof solution.

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Efficiency, Scalability, and Proactive Defense

By combining advanced automation with real-time insights, this feature helps organizations stay ahead of ever-evolving threats while optimizing resources.

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Efficiency

• Cuts down the time and resources traditionally required for security testing.
• Provides continuous, real-time attack remediation to keep systems secure without manual intervention.
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Scalability

• Seamlessly adapts to growing security needs as threats evolve.
• Agents “learn” and improve over time, ensuring your defenses stay ahead of adversaries.
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Proactive Risk Mitigation

• Uncovers vulnerabilities before they’re exploited, preventing costly breaches.
• Frees up security teams to focus on remediation and other critical tasks.
• The only solution of its kind that includes remediation.
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By adopting Lasso’s solution, enterprises gain a data-driven edge in security. The dashboard provides a continually updated snapshot of an organization’s performance across a wide range of relevant metrics. 

This data flywheel enables organizations to find issues and fix them iteratively, an invaluable advantage for businesses navigating new and evolving threats to their security. 

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DeepSeek Security Scores and Model Card Example:

Example output from the red teaming process for the popular open-source LLM that’s been making waves lately:

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🔻 Data leak protection: Weak

🔻 Hallucination prevention: Weak

⚠️ Guardrails on discussing China: Extra strong

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Proven Success: Automated Red Teaming is Already Delivering Results

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Through our work with global enterprises, we’ve uncovered critical gaps in LLM security, including the absence of a universal security benchmark for models and applications. These insights inspired us to develop a platform that provides continuous evaluation, security benchmarking, and the tools enterprises need to deploy and develop LLM-based applications safely.

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Our new red teaming feature (currently in Beta), delivers automated, scalable, and proactive security testing tailored to the unique challenges of LLM applications.

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Whether it’s empowering IT leaders to optimize AI infrastructure or supporting financial institutions in securing pre-production applications, this feature is already proving invaluable in identifying vulnerabilities, refining AI models, and setting new standards for LLM security.

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Take the first step toward safeguarding your AI systems: get in touch with Lasso today to learn how autonomous red teaming can build resilience into the foundations of your AI infrastructure.

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