Comprehensive security assessment of
AI-powered applications
Adopting generative AI and LLM agents introduces fundamental changes to application architecture that demand an entirely new approach to security testing. Traditional methods and tools fall short for AI systems. We find what others miss.
Why AI systems require special analysis
AI components differ from traditional applications in three key ways, each creating new security challenges.
Probabilistic behavior
LLMs may generate different responses to identical inputs — output is non-deterministic and can vary for the same input data. This makes typical deterministic tests inapplicable and requires adaptive testing methodologies that account for the probabilistic nature of AI.
AI-specific threat classes
Prompt injection, jailbreaking, data disclosure, integration errors, and excessive agent privileges — these threats are specific to AI systems and require specialized methodologies beyond the classic OWASP Top 10 for web applications.
Expanded attack surface
An AI agent becomes an autonomous execution unit with its own privileges. Multi-agent systems add further complexity — each new tool can become an attack vector, each agent connected to internal services can become a privilege escalation point. The attack surface expands dramatically beyond what traditional vulnerability scanners and WAFs can cover.
Real threats to AI applications
Each of these threats can lead to data compromise, business disruption, or reputational damage.
Prompt Injection
Attackers can hijack an AI agent through specially crafted inputs, gaining access to data and actions on behalf of the system.
Sensitive data leakage
An LLM may disclose sensitive information in its responses — user data, internal documents, API keys, or infrastructure details, including RAG database contents.
Business logic bypass
An agent's context may not account for various nuances of the implemented business logic, which an attacker can exploit to bypass restrictions even without specialized payloads (e.g., prompt injection attacks).
Excessive agent privileges
AI agents with overly broad access to tools and services create opportunities for large-scale attacks, including using integrated tools to gain access to internal infrastructure, launch spam campaigns, and conduct phishing.
Excessive resource consumption
Targeted overloading of system resources through AI components.
Who needs AI system assessment
Fintech & Banking
AI chatbots, scoring, anti-fraud — protection against manipulation and customer data leaks
SaaS & Product Companies
AI assistants, content generation, automation — protection against functionality abuse
E-commerce
AI recommendations, support chatbots — preventing customer data leaks
Healthcare
AI diagnostics, medical data processing — strictest confidentiality requirements
Government
AI systems for processing requests, analytics — protection of high-value information
Telecom & Industrial
AI for network and infrastructure management — security of critical systems
What we analyze
Our methodology covers all layers of AI-powered applications — from the user interface to internal agent integrations. The checklists below are indicative, and the specific scope of work is determined based on the assessment target. We also audit classic ML systems, using OWASP Top 10 for Machine Learning as a foundation and covering all lifecycle stages — from data preparation to production deployment.
Architecture & attack surface analysis
Comprehensive assessment from user interface and APIs to specific agents in multi-agent systems, including LLMs, integrations, and defense mechanisms.
- Identifying agent components and entry points for direct and indirect prompt injection
- Understanding agent business logic: goals, constraints, and tools used
- Analyzing data flows between users, LLMs, and tools
- Identifying implemented defense mechanisms
- Identifying critical paths in multi-agent systems
Specific vulnerability analysis
Testing in accordance with OWASP guidelines and methodologies for AI systems, including OWASP Top 10 for LLM Applications, OWASP Top 10 for Agentic Applications, OWASP MCP Top 10, as well as MITRE ATLAS and Google SAIF.
- Prompt Injection (direct and indirect)
- Insecure output handling
- Sensitive data disclosure
- Excessive resource consumption, etc.
Integration & access rights analysis
Reviewing connected integrations (resources, tools, RAG systems, other agents) and the privileges held by agents.
- Auditing tool and API access rights, including OWASP API Top 10
- Searching for known web vulnerability classes in APIs and the ability to craft payloads using LLMs
- Analyzing RAG systems for specific vulnerabilities
- Verifying authorization mechanisms when calling external services
- Analyzing horizontal and vertical privilege escalation possibilities
- Evaluating isolation between user sessions
- Testing for system prompt and configuration extraction
- Analyzing agent architecture and hierarchy (for multi-agent systems)
- Exploiting trust chains between agents (for multi-agent systems)
Defense mechanism analysis
Analyzing AI system components for hidden capabilities, potentially dangerous actions, or other excessive permissions.
- Jailbreak and content filter bypass
- Analysis of agent constraints
- Creating and testing agent abuse scenarios
- Testing business logic bypass scenarios, including multi-step request chains
GenAI functionality abuse
Identifying opportunities to abuse implemented AI functionality to violate application business logic.
- Manipulating agent behavior by exploiting system characteristics and weaknesses discovered in previous analysis stages
- Exploiting logical errors in agent chains
Assessment process
A structured process ensuring thorough analysis and transparency at every step.
Scoping
Agreeing on scope of work, identifying AI components, analyzing system documentation and architecture.
Architecture analysis
Determining the attack surface: LLM models, agents, integrations, APIs, user interfaces, and data flows.
Implementation analysis
Active vulnerability hunting following OWASP guidelines and methodologies for AI systems, MITRE ATLAS and Google SAIF, testing integrations, verifying defense effectiveness.
Vulnerability demonstration & risk analysis
Demonstrating real-world impact of discovered vulnerabilities, assessing severity and business impact.
Report & recommendations
Preparing a detailed report with technical details, severity ratings, and specific remediation recommendations.
What you receive
A detailed report covering identified risks, discovered weaknesses and their severity, along with recommendations for remediation and improving the overall security posture.
Executive summary
A brief overview of findings, key risks, and overall assessment of the AI system's security posture — for making strategic high-level decisions.
Technical report
Detailed description of each discovered vulnerability: root cause, reproduction steps, proof of exploitation (PoC), severity assessment considering CVSS and AI-specific factors.
Remediation recommendations
Specific and practical instructions for developers and DevOps teams to fix each vulnerability and improve the overall security posture.
Why SolidPoint
Our group of companies has over 10 years of experience in cybersecurity. The team conducts research at leading universities, with results presented at top conferences — OWASP AppSec, DEF CON, Black Hat, Hack in the Box, Positive Hack Days, OffZone, ZeroNights.
Research expertise
Founders and key team members are security researchers with publications in academic journals and presentations at leading conferences. Scanning technology presented at WASP at ESORICS 2023.
Proven results
20+ published CVEs in Apple, Google Chrome, VMware vCenter, MySQL2 products. Over 100 accepted reports in bug bounty programs of leading global companies.
Proprietary technology
SolidPoint discovers significantly more endpoints through client-side JavaScript analysis. Our proprietary SolidPoint DAST scanner provides dynamic analysis with intelligent validation.
Full spectrum of services
Beyond AI system penetration testing, we offer: SolidWall AI Security Gateway, SolidPoint DAST, SolidWall WAF, IT infrastructure security assessment, mobile application security, secure development training, DDoS & bot protection, and more.
Confidentiality
NDA signed before work begins. Strict adherence to all agreement terms. Secure deletion of all information upon project completion.
Industry recognition
Acknowledgments from Alibaba, Amazon, Apple, Google, IBM, PlayStation, Mail.ru and other companies for responsible vulnerability disclosure.
Critical vulnerabilities discovered by our team
Apple
CVE-2025-24192
Google Chrome
CVE-2023-5480 · CVE-2024-10229 · CVE-2025-4664
MySQL2 for Node.js
CVE-2024-21507 · CVE-2024-21508 · CVE-2024-21509
Frequently asked questions
How does AI system assessment differ from regular web application testing or penetration testing?
AI system assessment requires accounting for the non-deterministic nature of system components: the same inputs can produce different LLM outputs. While it may include testing for traditional web vulnerability classes (XSS, SQL injection, etc.), the primary focus is on finding AI-specific vulnerabilities, testing LLM constraint mechanisms, analyzing AI agent privileges, and identifying business logic bypasses through AI components. This requires specialized expertise and domain-specific methodologies.
What types of AI systems do you test?
We analyze a broad range of systems: web applications with LLM integration (ChatGPT, Claude, Gemini, etc.), multi-agent systems, RAG systems, AI chatbots and assistants, automation systems based on AI agents, and custom solutions built on open-source models.
Do you need access to source code?
Assessment is possible in Black Box, Grey Box, and White Box modes. For maximum coverage, we recommend Grey/White Box. However, even in Black Box mode, we identify critical vulnerabilities at the user interface and external API levels.
How is our data protected?
An NDA is signed before work begins. We guarantee complete protection of transferred data, strict adherence to the confidentiality agreement, and secure deletion of all information upon project completion. Testing can be conducted in an isolated environment.
What happens after we receive the report?
We provide consultation on remediation of discovered vulnerabilities and conduct a retest after fixes are implemented to confirm their effectiveness. If needed, advisory support during the remediation process is available.
Find vulnerabilities in your AI application before attackers do
Contact us to discuss a security assessment of your AI system. We will evaluate the scope of work and propose an optimal testing plan.