LLM/AI
Penetration Testing
Offensive security assessments for LLMs, AI agents, RAG pipelines, and AI-powered applications. Aligned with the OWASP LLM Top 10 2025.
True Adversarial AI Testing
Architecture-aware attacks beyond automated scanning
OWASP LLM Top 10 (2025) Coverage
Complete coverage of the latest LLM-specific vulnerabilities.
LLM01: Prompt Injection
User prompts that alter the LLM's intended behavior — bypassing safety guardrails, extracting sensitive data, or triggering unauthorized actions.
LLM02: Sensitive Information Disclosure
Sensitive information leaking through LLM outputs — exposing PII, credentials, proprietary data, or training data through model responses.
LLM03: Supply Chain
Vulnerabilities in third-party models, training data, plugins, and dependencies that introduce security risks into your AI pipeline.
LLM04: Data and Model Poisoning
Corrupted pre-training, fine-tuning, or embedding data that manipulates model behavior — introducing backdoors or degrading performance.
LLM05: Improper Output Handling
Insufficient validation and sanitization of LLM outputs that can lead to XSS, SSRF, privilege escalation, or remote code execution in downstream systems.
LLM06: Excessive Agency
LLM-based systems granted too much autonomy — executing unintended actions, accessing sensitive systems, or making decisions beyond their intended scope.
LLM07: System Prompt Leakage
Extraction of system prompts that reveal internal logic, security controls, access patterns, or sensitive configuration details.
LLM08: Vector and Embedding Weaknesses
Security risks in vector databases and embedding systems — including poisoned embeddings, retrieval manipulation, and unauthorized access to knowledge bases.
LLM09: Misinformation
LLM-generated false or misleading content that applications rely on for decision-making — hallucinations, fabricated data, and confident but incorrect outputs.
LLM10: Unbounded Consumption
Resource exhaustion through unconstrained LLM usage — denial of wallet attacks, excessive token consumption, and computationally expensive queries.
Attack Techniques
Advanced adversarial techniques for comprehensive AI security testing.
Direct & Indirect Prompt Injection
Bypass safety guardrails via user input or poisoned external data sources like documents, web pages, and emails.
Jailbreaking & Guardrail Bypass
Creative attacks that circumvent content policies, safety filters, and alignment through context manipulation.
System Prompt Extraction
Techniques to extract system prompts, revealing internal logic, security controls, and sensitive configuration.
RAG Pipeline Exploitation
Attacks targeting retrieval-augmented generation — poisoning knowledge bases, manipulating retrieval, and exfiltrating data.
Agent & Tool Abuse
Exploit agentic AI workflows to trigger unauthorized tool calls, escalate privileges, or access sensitive systems.
Output Exploitation
Test how downstream systems handle LLM outputs — finding XSS, SSRF, command injection, and other vulnerabilities.
Why AI Security Matters
AI introduces attack surfaces traditional testing cannot detect.
Prevent Prompt Injection
Identify vulnerabilities that allow attackers to manipulate AI behavior through crafted inputs.
Protect Proprietary Logic
Prevent extraction of valuable model weights, architecture, or training methodologies.
Identify Data Leakage
Find vulnerabilities that expose training data or sensitive information through model outputs.
Free Retesting
Complimentary retest of all findings within 30 days to validate remediation.
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