The definitive security assessment for enterprise LLM deployments — covering all 10 OWASP LLM vulnerability classes with automated scanning, expert manual testing, and actionable remediation guidance.
Enter your LLM API endpoint or model type and get an instant OWASP LLM Top 10 risk assessment — all 10 vulnerability categories scored in real-time.
Every assessment covers the complete OWASP LLM Top 10 — automated detection plus expert manual validation for complex attack scenarios.
Attackers manipulate LLM behavior by crafting inputs that override system instructions. Direct injection targets the model directly; indirect injection plants malicious instructions in external data retrieved by the LLM (RAG systems, web browsing, tool outputs).
CRITICALLLM-generated content passed unsanitized to downstream systems enables XSS, CSRF, SSRF, privilege escalation, and remote code execution. Common in LLM-integrated web apps and code execution environments.
CRITICALAdversarial manipulation of training datasets, fine-tuning data, or RLHF feedback to introduce backdoors, biases, or malicious behavior patterns that activate on specific trigger inputs.
HIGHAdversarial inputs that cause resource exhaustion — extremely long context windows, recursive prompts, computationally expensive reasoning chains — degrading service availability and increasing operational costs.
HIGHRisks from third-party model providers, pre-trained base models, plugins, datasets, and deployment infrastructure. Includes compromised model weights, malicious fine-tuning datasets, and vulnerable LLM framework dependencies.
HIGHLLMs inadvertently revealing training data (memorization attacks), system prompts, API keys in context, or confidential business information embedded in fine-tuning datasets or retrieved via RAG systems.
CRITICALLLM plugins and function-calling tools with insufficient input validation, overly broad permissions, or inadequate authentication enabling unauthorized data access, privilege escalation, and lateral movement.
HIGHLLM-powered agents with excessive permissions, capabilities, or autonomy taking harmful actions beyond their intended scope — deleting files, sending emails, executing transactions, or modifying configurations without authorization.
CRITICALOrganizational risk from excessive trust in LLM outputs without verification — leading to security decisions based on hallucinated vulnerability data, incorrect compliance guidance, or fabricated threat intelligence.
MEDIUMUnauthorized extraction of model architecture, weights, or training data through API abuse (model extraction attacks), side-channel analysis, or membership inference — enabling competitors to replicate proprietary AI without authorization.
HIGHAutomated scanning combined with expert manual testing — no checkbox compliance, real attack simulation.
Automated test suite executes 100+ prompt injection payloads, output handling tests, and information disclosure probes against your LLM API endpoints.
Security engineers manually test complex attack chains — multi-turn prompt injection, indirect injection via RAG, plugin chaining attacks — that automated tools miss.
Every finding scored with CVSS v3.1, detailed proof-of-concept, business impact analysis, and prioritized remediation guidance — delivered within 72 hours.
Post-fix retesting validates that remediation is effective and doesn't introduce new vulnerabilities. Included in the assessment at no additional cost.
Board-ready executive summary with risk heat map, business impact scoring, and budget justification language for security investment decisions.
Findings mapped to NIST AI RMF, ISO 42001, EU AI Act requirements — turn assessment results directly into governance documentation.
The integration of LLMs into enterprise workflows has dramatically expanded the attack surface. Traditional application security — input validation, authentication, authorization — remains necessary but is no longer sufficient. LLMs introduce fundamentally new attack vectors: the model itself becomes an attack surface through its ability to interpret and act on natural language instructions embedded in any input it processes.
High-profile incidents have validated these risks: LLM-powered chatbots manipulated into providing harmful content, RAG systems leaking confidential documents through carefully crafted queries, and AI agents performing unauthorized transactions after receiving indirect injection payloads in retrieved web pages.
Prompt injection attacks exploit the fundamental tension in LLM design: the model must treat both instructions (system prompt) and data (user input, retrieved content) as natural language, making it inherently difficult to distinguish between "instruction to follow" and "data to process." Modern defenses include input sanitization, output filtering, privilege separation, and instruction hierarchy enforcement — but none are 100% reliable against sophisticated attackers.
Our assessment tests direct injection (user-controlled input), indirect injection (retrieved external content), multimodal injection (images containing embedded text instructions), and instruction hierarchy bypass techniques against your specific LLM implementation and deployment architecture.
Enterprise OWASP LLM assessments with 72-hour delivery. Trusted by AI security teams globally.
Starting ₹9,999 · 72-hour delivery · CVSS-scored findings · Remediation verification included