Secure Your AI Systems Before Attackers Exploit Them
AI and LLM-based applications face both traditional vulnerabilities and novel AI-specific attack vectors. From prompt injection to data poisoning, we identify and help you remediate the security risks unique to machine learning systems.
Discuss Your AI Security NeedsAI Security Challenges We Address
AI systems introduce attack surfaces that traditional security tools miss. We specialize in finding and fixing these emerging threats.
Prompt Injection Attacks
Adversaries can manipulate your LLM to execute unintended actions, bypass safety measures, or leak sensitive information through carefully crafted inputs.
Data Poisoning & Leakage
Training data can be compromised, and models may inadvertently memorize and expose sensitive information from their training corpus.
Excessive Model Agency
AI agents with too much autonomy can be manipulated to access unauthorized resources, execute code, or take actions beyond their intended scope.
Supply Chain Vulnerabilities
Pre-trained models, third-party APIs, and ML pipelines introduce dependencies that can be vectors for compromise if not properly secured.
We Audit the Entire AI Stack
From data collection to model deployment, we assess every layer of your AI infrastructure for security vulnerabilities.
AI Agents & Assistants
Agents that import data from remote sources present attack surfaces adversaries will exploit. We assess agent architectures for manipulation vulnerabilities and privilege escalation paths.
Model Training Pipelines
Training involves multiple data sources, modules, and users with varying permissions. We identify supply-chain attack vectors and data integrity risks throughout the training process.
Code & Content Generation
When LLMs generate code or content that flows to other systems, adversaries can inject malicious payloads. We assess generation pipelines for injection and manipulation risks.
AI Application Infrastructure
Whether you expose models via APIs or deploy in microservice architectures, we audit the infrastructure layer for traditional and AI-specific vulnerabilities.
Our AI Security Services
Comprehensive security assessment tailored to your AI implementation.
Threat Modeling
We map your AI system's attack surface, identify threat actors, define trust boundaries, and evaluate existing security controls.
Red Team Assessment
We take an attacker's perspective to attempt data theft, model manipulation, infrastructure compromise, and user exploitation.
Manual Code Audit
Deep manual review of your AI infrastructure and applications for security risks, misconfigurations, and vulnerabilities.
Automated Testing
We leverage state-of-the-art fuzzing, static analysis, and dynamic analysis tools to complement our manual review.
Ongoing Assessments
Regular security audits on a yearly or semi-annual basis to catch vulnerabilities as your AI systems evolve.
Security Hardening
We work alongside your team to implement fixes and strengthen the security posture of your AI infrastructure.
How We Work
A structured approach to securing your AI systems.
Scoping
We understand your AI architecture, use cases, and security concerns to define the assessment scope.
Assessment
Our researchers conduct thorough manual and automated testing of your AI systems.
Reporting
Clear documentation of findings with severity ratings, reproduction steps, and remediation guidance.
Remediation
We support your team through fixes and verify that vulnerabilities are properly addressed.
Ready to Secure Your AI Systems?
Let's discuss your AI security needs. Whether you're deploying a new LLM application or want to assess existing infrastructure, we're here to help.
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