Back to all blog  posts

Red & Blue Teaming for GenAI Risks

Ophir Dror
Ophir Dror
Ante Gojsalić
Ante Gojsalić
calendar icon
Thursday
,
August
29
clock icon
5
min read
On this page

As Generative AI (GenAI) continues to reshape industries, it brings along a new set of cybersecurity challenges. While the potential of GenAI is immense, the rapid adoption of these technologies introduces vulnerabilities that can be exploited by malicious actors, causing damage to an organization’s brand reputation and legal liabilities. Organizations must now balance the innovative capabilities of GenAI with robust security measures to mitigate these emerging risks.

Understanding the GenAI Security Landscape

The statistics paint a clear picture:

  • 1.5 billion+ global users engage with conversational AI applications.
  • 45% of companies hesitate to implement chatbots due to privacy, security, and legal concerns.
  • 64% of non-users would start using GenAI if it were safer.
  • 4X - The rise in chatbot attacks due to GenAI vulnerabilities has quadrupled annually.

These figures underscore the urgent need for comprehensive security strategies to protect GenAI implementations from evolving threats.

The Challenges: Navigating the Wild West of GenAI Security

Generative AI (GenAI) poses unique risks requiring vigilant management. OWASP as other researchers highlights key vulnerabilities in Large Language Models (LLMs):

Risk Description Example How to Identify How to Prevent
Prompt Injection Malicious inputs causing unintended outputs. A disguised command in a chatbot revealing sensitive info. Simulate Adversarial user. Automated AI Pentesting Input validation, strict input guidelines, context-aware filtering.
Insecure Output Handling Failing to sanitize or validate outputs, leading to attacks. An LLM-generated script causing an XSS attack. Identify gaps in outputs based on your policies and guidelines. Sanitize outputs, encode and escape outputs.
Training Data Poisoning Manipulation of training data to skew model behavior. Biased data causing a financial LLM to recommend poor investments. Test the model’s behavior in various relevant scenarios. Detect anomalies. Validate and clean data, and detect anomalies.
Model Denial of Service Overwhelming the LLM with requests causing slowdowns or unavailability. Resource-intensive queries overloading the system. Perform automated AI pentesting and manual AI Red Teaming tailored for your model and system. Rate limiting, monitor queues, optimize performance, and scaling strategies.
Supply Chain Vulnerabilities Compromised third-party services or resources affecting the model. Tampered data from a third-party provider manipulating outputs. Use SBOMs and AI SAST scanners. Track vulnerabilities in frameworks integrated into your AI system. Security audits, monitor behavior, trusted supply chain.
Sensitive Information Disclosure LLMs revealing confidential data from training materials. Responses containing PII due to overfitting on the training data. Simulate regular and adversarial user conversations focused on PII extraction. Do AI Red Teaming. Anonymize data, enforce access control with AI Firewall.
Insecure Plugin Design Vulnerabilities in plugins or extensions. A third-party plugin causing SQL injections. Do tailored AI Red Teaming for each integrated plugin or extension. Security reviews, follow coding standards.
Excessive Agency LLMs making uncontrolled decisions. An LLM customer service tool making unauthorized refunds. Do SAST and DAST analysis of decision rights in AI Model. Limit LLM decisions, and human oversight.
Overreliance Overreliance on LLMs for critical decisions without human oversight. An LLM making errors in customer service decisions without review. Identify skill gaps of system users and their AI Safety Awareness. Human review of LLM outputs, supplemented with data inputs.
Model Theft Unauthorized access to proprietary LLM models. A competitor downloading and using an LLM illicitly. Do AI Model Red Teaming. Authentication, encrypt data, access control.

Addressing these requires continuous monitoring, security assessments, and integrated offensive and defensive strategies.

The Solution: Red and Blue Teaming Synergy

Addressing these challenges demands a comprehensive security approach that integrates both offensive (red teaming) and defensive (blue teaming) measures. This synergy is where Lasso Security and SplxAI excel, providing a dual-layered security mechanism tailored for GenAI environments.

Blue Teaming: Defense and Mitigation with Lasso Security

Lasso Security’s blue teaming solutions focus on safeguarding GenAI applications and Large Language Models (LLMs) against a wide array of cyber threats:

  • Always-on Shadow LLM™: Uncover every LLM interaction, allowing precise identification of active tools, models, and users within an organization.
  • Real-Time Response and Automated Mitigation: Swift alerts and automated defenses ensure rapid responses to real-time threats.
  • Tailored Policy Enforcement: Organizations can implement customized security policies that align with their unique regulatory requirements.
  • Privacy Risk Reduction: Data protection is prioritized from the initial deployment stages, ensuring long-term security compliance.

Red Teaming: Offensive Security with SplxAI

SplxAI specializes in AI red teaming, an offensive security practice that simulates real-world attacks to identify and exploit vulnerabilities in GenAI applications:

  • Automated Scans: Reduce the need for manual testing, providing continuous, on-demand protection against evolving threats.
  • Compliance Mapping: Assess conformance to critical AI security frameworks such as OWASP LLM Top 10, MITRE ATLAS, and GDPR.
  • Comprehensive Reporting: Detailed insights into vulnerabilities, their potential impact, and recommended remediation steps.
  • Continuous Improvement: Iterative red teaming ensures that organizations stay ahead of emerging threats, continuously refining their security posture.

Next Steps: Maximizing the Value of Red and Blue Teaming Synergy

Lasso Security’s blue teaming and SplxAI’s red teaming combine to form a holistic defense strategy, identifying vulnerabilities and providing robust defenses:

Enhanced Security

Addressing both offensive and defensive needs results in a stronger security posture.

Strategic Insights

Expertise informs long-term security planning and investment.

Continuous Risk Management

A comprehensive approach ensures risks are continuously identified and mitigated.

Compliance

Iterative measures keep practices aligned with evolving threats.

Download Solution Brief