DiscoveR

DiscoveR

Continuous Vulnerability scanning for Gen AI 

Continuous Vulnerability scanning for Gen AI 

Protect your AI systems against emerging threats that traditional application security tools fail to tackle. 

Protect your AI systems against emerging threats that traditional application security tools fail to tackle. 

Key Features

Key Features

Comprehensive Attack Scenarios 

Comprehensive Attack Scenarios 

Covers comprehensive multile attack senarios ranging from prompt injection attack, PII leaks, bias, toxicity to vector database attacks, model extraction, tools / plugin exploits, knowledge extraction, context manipulation, function calling exploits, covering standard chat bot, RAG to AI agentic behaviour exploitation. 

Risk Assessment 

Risk Assessment 

Thoroughly examines your AI/ML operations lifecycle and critical models to identify potential risks. Leveraging industry standards like NIST, MITRE ATLAS, and OWASP, we deliver actionable insights to strengthen your security posture and safeguard your organization. 

Seamless Integration 

Set up attack scenarios in minutes to simulate real-world threats targeting your AI models and systems. Run the tool independently or integrate it directly into your CI/CD pipeline for continuous security testing. 

Attack Paths 

Attack Paths 

AI systems are vulnerable to threats across various stages of their pipeline. Key attack paths with visual cues provided to highlight the exploitation points. This comprehensive visualization of attack paths ensures organizations understand where threats can originate and how to implement robust security measures to protect their AI pipelines. 

Compatible with all LLMs

Comprehensive Attack Scenarios in GenAI Systems

  • Prompt Injection Attacks 

    Manipulation of system and user prompts to modify AI behavior maliciously. 

    Injected prompts trick the model into revealing sensitive information or executing harmful instructions. 

  • PII Leaks 

    Unintended disclosure of Personally Identifiable Information (PII) during interactions. 

    Lack of proper anonymization or encryption in the AI pipeline leading to privacy violations. 

  • Bias Exploitation 

    AI models reflecting and amplifying inherent biases from training data. 

    Adversaries exploiting biased outputs to cause reputational or legal damage. 

  • Toxicity Amplification 

    AI systems generating harmful or offensive outputs, damaging user trust. 

    Triggered through carefully crafted inputs designed to provoke toxic responses. 

  • Vector Database Attacks 

    Reverse engineering embeddings to recover original data. 

    Metadata exploitation leading to leakage of contextual information. 

    Unauthorized Access: Insider threats or hackers accessing sensitive embeddings. 

  • Model Extraction Attacks 

    Adversaries extracting or replicating the proprietary model by querying it extensively. 

    Intellectual property theft leading to competitive disadvantages. 

  • Tools/Plugin Exploits

    Exploitation of vulnerabilities in third-party tools or plugins integrated with the AI system. 

    Malicious plugins gaining unauthorized access to sensitive AI workflows or data. 

  • Knowledge Extraction 

    Adversaries crafting inputs to extract proprietary or confidential information encoded in the model. 

    Exploiting the model’s memorized training data for sensitive information. 

  • Context Manipulation 

    Manipulating the conversational or retrieval context in RAG (Retrieve and Generate) workflows. 

    AI systems providing inaccurate or misleading outputs due to tampered context. 

  • Function Calling Exploits 

    Abusing function-calling APIs in AI agents to trigger unintended system-level actions. 

    Elevation of privileges or execution of unauthorized tasks by exploiting weak safeguards. 

  • AI Agentic Behavior Exploitation 

    Manipulating multi-agent systems to compromise goals or outputs. 

    AI agents conflicting with organizational objectives due to adversarial agent behavior.

  • RAG-Specific Attacks 

    Poisoning document ingestion in RAG pipelines to inject malicious data. 

    Unauthorized document access due to improper role-based access controls. 

  • Hallucination Exploits 

    Exploiting AI-generated hallucinations to propagate misinformation or bypass validation. 

    Using hallucinations to disguise malicious intent within legitimate-looking outputs. 

  • Adversarial Inputs 

    Carefully crafted adversarial inputs leading to erratic model behavior. 

    Exploits leveraging model weaknesses to output false or misleading results. 

  • Secure Function Exploitation 

    Triggering insecure function execution in system workflows using AI agent capabilities. 

    Exploiting weak or misconfigured APIs that the AI interacts with. 

  • Data Poisoning 

    Corrupting training or retraining datasets to compromise model integrity. 

    Subtle manipulations leading to targeted outputs or performance degradation. 

Get your AI Security Risk Report, Now!

Get your AI Security Risk Report, Now!

Get your AI Security Risk Report, Now!

Start your Assessment

Start your Assessment

Let's Talk how we can provide continuous vulnerability scanner!

Let's Talk how we can provide continuous vulnerability scanner!

We’re excited to connect with you! Our founders are available for personalized meetings to discuss Mirror Security, AI protection strategies, and how we can help your business.

We’re excited to connect with you! Our founders are available for personalized meetings to discuss Mirror Security, AI protection strategies, and how we can help your business.

Schedule a Call

Schedule a Call

Schedule a Call

Schedule a Call

Mirror Security

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Mirror Security

© All right reserved