AI Security | SecuraAI

Generative AI Security

Generative AI systems, capable of creating human-like text, images, code, and more, are revolutionizing industries but also introduce unprecedented security risks. Protecting these systems requires strategies that ensure the integrity, confidentiality, and reliability of their data, models, and processes. As adoption soars, risks such as adversarial attacks, data breaches, and ethical challenges grow in complexity, threatening trust and operational safety. Adherence to frameworks like NIST and the EU AI Act provides foundational guidance, while OWASP’s LLM Top 10 addresses vulnerabilities specific to language models, such as prompt injection and data leakage. Complementing these, MITRE ATLAS enhances defenses by mapping adversarial tactics and techniques. These measures are critical to safeguarding assets and responsibly navigating the generative AI era.

Our AI Security Solutions

Generative AI

Systems that create new content, such as text, images, audio, or code, by learning patterns from vast datasets (Training) and responding to user inputs in innovative and meaningful ways (Inference).

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Generative AI Security Risks

Prompt Injection

Manipulative prompts alter the model’s behavior or outputs, potentially bypassing safety mechanisms or causing harmful actions.

Data and Model Poisoning

Malicious manipulation of training or fine-tuning data embeds vulnerabilities, backdoors, or biases, compromising model security and behavior.

System Prompt Leakage

Exposure of backend prompts used to guide LLM behavior.

Unbounded Resource Consumption

Poorly configured LLMs are exploited to drain computational resources, leading to service disruption or high operational costs.

Sensitive Information Disclosure

LLMs inadvertently expose sensitive data, such PII, proprietary algorithms, or confidential business information, due to insufficient data sanitization.

Improper Output Handling

Insufficient validation of LLM outputs leads to vulnerabilities like XSS, SQL injection, or unintended code execution.

Vector and Embedding Weaknesses

Exploiting vulnerabilities in embeddings or feature representations.

Supply Chain Risks

Third-party dependencies, pre-trained models, or fine-tuning methods introduce risks, including tampered components and outdated models.

Excessive Agency

LLMs are granted too much autonomy, enabling unintended or harmful decisions without sufficient human oversight.

Misinformation

LLMs generating or amplifying false or misleading content.

AI System means a machine-based system that is designed to operate with varying levels of autonomy and that may exhibit adaptiveness after deployment, and that, for explicit or implicit objectives, infers, from the input it receives, how to generate outputs such as predictions, content, recommendations, or decisions that can influence physical or virtual environments.

Types of Generative AI Systems

Autonomous Agents

AI-driven systems that perform tasks with minimal human intervention.
Common Risks:
- Excessive Agency (LLM06): Overautonomy causing unintended actions.
- Prompt Injection* (LLM01): Manipulated inputs altering behavior. (*Applicable to most AI systems)
Examples: Virtual assistants, task automation bots.

RAG Applications

Systems combining LLMs with external knowledge bases for enhanced responses.
Common Risks:
- Vector and Embedding Weaknesses (LLM08): Data leakage from embeddings.
- Data and Model Poisoning (LLM04): Altered or biased outputs from compromised data sources.
Examples: Knowledge management systems, AI-enhanced search engines.

Multi-Tenant AI Platforms

Shared LLM infrastructure supporting multiple users or organizations.
Common Risks:
- Sensitive Information Disclosure (LLM02): Cross-tenant data leakage.
- System Prompt Leakage (LLM07): Exposure of backend prompts.
Examples: SaaS-based LLM APIs, collaborative AI platforms.

Chatbots and Conversational AI

Interactive systems designed for real-time communication with users.
Common Risks:
- Prompt Injection (LLM01): Manipulated prompts causing harmful outputs.
- Improper Output Handling (LLM05): Generation of offensive or dangerous responses.
Examples: Customer support bots, virtual help desks.

Content Generation Tools

AI applications creating text, images, or videos based on prompts.
Common Risks:
- Misinformation (LLM09): Spreading false or misleading content.
- Improper Output Handling (LLM05): Unsafe or biased content creation.
Examples: Text generators, image synthesis tools, video creators.

Code Generation and Automation Tools

Systems that assist in programming or automating workflows.
Common Risks:
- Improper Output Handling (LLM05): Generating insecure code.
- Unbounded Resource Consumption (LLM10): Excessive API usage causing system overload.
Examples: Coding assistants, workflow automation platforms.

Multimodal Systems

AI systems that process and generate outputs using multiple modalities, such as text, images, video, or audio.
Common Risks:
- Vector and Embedding Weaknesses (LLM08): Exploitable vulnerabilities in embeddings, allowing attackers to reverse-engineer multimodal data or inject adversarial noise.
- Data and Model Poisoning (LLM04): Manipulation of training datasets across different modalities, leading to biased or harmful outputs.
- Improper Output Handling (LLM05): Generating unsafe or inappropriate content due to insufficient validation across modalities.
- Sensitive Information Disclosure (LLM02): Leakage of cross-modal data, such as associating confidential textual information with image or video outputs.
Examples: Text generators, image synthesis tools, video creators.

Securing AI isn't just about protecting data—it's about safeguarding the integrity of decisions that shape our world

Our AI Security Solutions

Generative AI Vulnerability Scanner

Protect your AI systems with our cutting-edge Generative AI Vulnerability Scanner. This powerful tool automatically detects and flags potential security risks in your AI models, from prompt injection threats to data leakage vulnerabilities. Stay ahead of emerging AI security challenges and ensure your generative AI deployments remain secure and reliable.

Features

Our Generative AI Vulnerability Scanner identifies and mitigates security risks in AI systems, ensuring robustness, compliance, and trust. With advanced scanning capabilities and actionable insights, it helps secure your models, data, and outputs against evolving threats.

Comprehensive Threat Detection

Identify vulnerabilities in models, training data, and outputs, including adversarial threats, data leaks, and poisoning risks.

Standards-Aligned Scanning

Ensure compliance with industry standards like NIST and OWASP by scanning for vulnerabilities mapped to regulatory frameworks.

Actionable Insights and Mitigations

Receive detailed reports with prioritized risk scores and integrated recommendations for immediate remediation.

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With the EU AI Act's transformative impact on AI regulation, preparing early is critical. Proactive readiness assessments and tailored compliance strategies will save organizations time, cost, and complexity down the line.

Evaluate and Classify AI Systems

Catalog and Document AI Assets

Establish Governance and Risk Management Frameworks

Adversarial Attack Simulation Platform

Safeguard your AI assets with our Attack Simulation Platform, designed to simulate real-world attacks on models, training datasets, and more. Featuring a suite of advanced attack engines, integrated mitigations, and measurable metrics mapped to industry standards and regulatory requirements, it offers a comprehensive solution for assessing and strengthening AI security.

Features

Our Attack Simulation Platform enables you to test and enhance the security of your AI assets by simulating real-world threats. With advanced attack engines and integrated mitigations, it ensures your AI systems remain resilient against evolving vulnerabilities.

Diverse Attack Scenarios

Simulate a wide range of threats, including adversarial inputs, model poisoning, and data breaches, to assess your AI system’s defenses

Metrics and Standards Mapping

Evaluate vulnerabilities using predefined metrics aligned with industry standards and regulatory requirements like NIST and OWASP.

Integrated Mitigation Strategies

Access built-in recommendations and best practices to address detected vulnerabilities, ensuring a faster path to securing your AI systems.

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AI Threat Intelligence Dashboard

Proactively identify, prioritize, and mitigate cyber threats with real-time visibility, actionable insights, and trend analysis. Leverage advanced monitoring and instant alerts to anticipate and respond to emerging risks, ensuring your organization stays resilient against evolving attack vectors.

Features

Our AI Threat Intelligence Dashboard empowers you to stay ahead of potential risks by delivering real-time threat detection and actionable insights. By leveraging advanced analytics and AI-driven intelligence, the dashboard ensures your organization is equipped to mitigate and neutralize emerging threats effectively.

Comprehensive Threat Monitoring

Simulate a wide range of threats, including adversarial inputs, model poisoning, and data breaches, to assess your AI system’s defenses

Actionable Insights & Alerts

Access AI-curated recommendations and instant alerts, enabling your security team to prioritize and respond to critical risks with precision.

Threat Trends Analysis

Track and analyze emerging threat trends over time, helping your organization proactively adapt its defenses and anticipate potential future attack vectors.