AI & LLM Security Testing
Isolate model alignment vulnerabilities and secure cognitive pipelines. Our elite AI & LLM Security Testing (VAPT) services audit direct prompt parameters, prevent indirect prompt injections, evaluate RAG vector corridors, map plugin boundaries, and stress-test autonomous agency.
"AI & LLM Security Testing is the specialized technical validation of cognitive applications, base weights, and integration pipelines. By simulating complex jailbreaks and parsing multi-tenant vector boundaries, we build ironclad trust around autonomous agent workflows."
Unlike traditional software APIs that execute deterministic logical loops, Large Language Models (LLMs) operate on probabilistic, semantic parameters. This architectural open-endedness introduces entirely new attack classes. Threat actors do not need to exploit code buffers; they can simply chat with your system, using natural language prompt injection to bypass system controls, siphon proprietary RAG archives, or hijack downstream APIs.
Our comprehensive AI security service audits your deployment across every layer. We stress-test input boundaries to block prompt hijackings, verify the integrity of vector search databases, map autonomous plugin authority, and validate output handling to prevent reflected XSS or RCE. The result is a resilient, secure, and compliant enterprise AI ecosystem.
"You cannot secure a probabilistic model with deterministic code alone— active guardrail validation and strict agency control are the only ways to ensure your autonomous agents behave under pressure."
Safety Containment: Securing AI Gateways and Blocking Injection Vector Spills
Cognitive Pipeline Security Visualization
The Critical Risks of Insecure AI Systems
System Guardrail Overrides
Adversarial prompts bypass structural alignments, causing models to reveal raw system templates or execute rogue commands.
RAG Semantic Poisoning
Attackers inject corrupt metadata or poisoned search chunks into RAG paths, silently poisoning output logic.
Autonomous Plugin Hijackings
Excessive agency lets models convert injected text into active API updates, leading to unauthorized operations.
The Evolving AI & LLM Threat Landscape
Derived from OWASP Top 10 for LLMs & MITRE ATLAS™ Framework
Direct Prompt Injection
Crafting adversarial inputs to bypass system alignments and force the LLM to execute prohibited, out-of-bounds commands.
Indirect Prompt Injection
Injecting malicious instructions into remote external files, web resources, or databases queried by RAG pipelines to hijack sessions.
Training Data Poisoning
Corrupting fine-tuning datasets or base pre-training pools to introduce logic backdoors or safety-bypass trigger words.
Insecure Output Handling
Allowing raw model outputs to render directly in UI templates, enabling malicious Cross-Site Scripting (XSS) or remote command executions.
Model Denial of Service
Flooding AI engines with recursively complex prompts or highly padded context scopes to spike resource usage and exhaust GPUs.
Sensitive Data Disclosure
Extracting hidden system guidelines, proprietary prompt templates, or training PII through targeted extraction attacks.
Insecure Plugin Design
Exploiting overprivileged tool integrations that accept unvalidated text strings from the model to execute state-changing actions.
Excessive Agency Gaps
Permitting autonomous agents to trigger destructive, unvalidated file operations or API callbacks without human-in-the-loop gates.
Overreliance Vulnerability
Failing to validate synthetically generated codes or logic inputs, resulting in silent code-execution compromises.
Model Theft & Extraction
Exfiltrating private weights, proprietary parameters, or structural configurations through high-volume API queries.
Why AI & LLM Security Testing is Critical
Deploying resilient cognitive structures requires active verification. AI penetration testing isolates input injection vectors, verifies RAG storage parameters, limits plugin scopes, and ensures model interactions align with strict enterprise safety protocols.
Block Injection & Jailbreaks
Stress-test prompt filters and system guardrails, locking down model behaviors against adversarial hijackings.
Secure Semantic RAG Corridors
Validate vector endpoints and tenant tokens to prevent data leakage and metadata poisoning within vector databases.
Isolate Plugin & Agency Authority
Enforce granular permissions and strict authorization controls on autonomous agent tools and down-stream API relays.
Build Customer AI Trust
Demonstrate safety alignment to compliance bodies, satisfying key SOC 2 trust principles and upcoming AI regulations.
Comprehensive Assessment Scope
Our rigorous AI penetration testing scope covers the three critical dimensions of cognitive system security: Prompt & Safety Guardrail Auditing, RAG & Vector Database Integrity, and Autonomous Integration & Agency Controls.
Prompt Injection & Safety Guardrail Auditing
Rigorous stress-testing of direct prompt structures, system instruction barriers, and alignment filters to prevent jailbreaks.
RAG & Vector Database Integrity Auditing
Detailed security analysis of retrieval-augmented corridors, vector database nodes, and search indexing mechanics.
Integrations, Plugins & Agency Verification
Active safety validation of tool invocation bounds, overprivileged plug-ins, and autonomous agent loops.
Our AI Testing & VAPT Methodology
Our highly structured VAPT lifecycle combines static prompt-wall mapping, active jailbreak attacks, vector boundary testing, and plugin privilege sweeps to evaluate your AI model deployments.
Discover & Threat
Modeling
We map out AI endpoints, model specifications, system prompts, retrieval context layouts, and autonomous plugin parameters.
Jailbreak & Prompt
Injection
We perform automated and manual adversarial prompt injections, evaluating model alignments against system overrides.
RAG & Vector DB
Stress-Testing
We audit semantic vector corridors, simulating database exfiltrations, cache poisonings, and metadata injections.
Plugin & Tool
Agency Sweeps
We evaluate autonomic tool triggers, executing state-changing relays to check verification blocks and token bounds.
Harden &
Verify
We supply secure prompt templates and network GPOs, conducting secondary sweeps to supply a CERT-In backed attestation.
What You Receive
We deliver actionable prompt injection reports, vector DB trust maps, secure system templates, and signed compliance attestations.
"Detailed findings of prompt boundary audits, guardrail bypasses, insecure output reflects, and step-by-step proof-of-concepts."
"Detailed findings of prompt boundary audits, guardrail bypasses, insecure output reflects, and step-by-step proof-of-concepts."
Why Choose Arridae
We combine deep cognitive threat intelligence with accredited penetration expertise to safely validate your AI models, vector stores, and automated plugin tools.
Expert CERT-In AI Audit Specialists
Assessments directed strictly by CERT-In accredited specialists with extensive experience auditing model guardrails and vector DB endpoints.
Adversarial ATLAS Mapping
Assessments mapped fully to MITRE ATLAS™ and OWASP LLM matrices to capture all emerging cognitive threat vectors.
Advanced Prompt Injection Engine
We deploy state-of-the-art fuzzers and manual jailbreak models to discover hidden safety boundary overrides.
Zero Operational Downtime
All automated prompt surges and context boundary stress-tests are safely limited to protect live GPU node capacities.
Granular Vector DB Inspections
Deep validation of tenant-isolation token mechanics, database indices, and secure RAG data corridors.
Compliance-Ready certified Attestations
Receive certified reports signed by accredited auditors to easily satisfy SOC 2 and international AI regulatory standards.
“AI models introduce cognitive, probabilistic pathways— sandboxed plugin architectures and resilient guardrails are what secure the core when prompts leak. ”
Harden Model Guardrails and Secure Your AI Pipelines Today
AI Security Testing Q&A
Commonly asked questions about LLM penetration testing, prompt injections, vector DB safety, autonomous plugin agency, and our methodology.