What Is an AI Security Assessment?
An AI security assessment is a structured evaluation of the security posture of AI-powered applications — LLMs, autonomous agents, RAG systems, and AI copilots. Its purpose is to identify vulnerabilities that attackers could exploit, measure compliance readiness, and provide prioritised recommendations before an incident occurs.
Unlike traditional application security assessments, AI security assessments focus on risks that are unique to large language models and agentic systems: prompt injection, retrieval poisoning, agent tool abuse, data exfiltration through model outputs, and excessive autonomous authority. These attack surfaces don't exist in non-AI applications, which is why traditional security programs consistently miss them.
💡 Key distinction: Traditional security assessments test whether your code has vulnerabilities. AI security assessments test whether your model's behaviour can be manipulated — a fundamentally different attack surface that requires different testing methods.
Why AI Security Is Different From Traditional Security
Traditional applications separate instructions from data. Large language models process both simultaneously. This is the root cause of AI-specific vulnerabilities: an attacker can craft input that functions as both data and instruction, influencing the model's behaviour without touching any code.
Examples of AI-specific attacks that traditional security programs miss entirely:
- Prompt Injection (OWASP LLM01) — adversarial inputs override system instructions, causing the model to follow attacker directives instead of its configured behaviour
- Indirect Prompt Injection — malicious instructions embedded in documents, emails, or web pages that the AI retrieves and executes invisibly
- RAG Poisoning — adversarial content planted in the knowledge base that influences AI responses when retrieved
- Agent Tool Abuse (MITRE ATLAS T0054) — AI agents manipulated into calling tools with attacker-controlled arguments
- Data Exfiltration via Output — sensitive information extracted through carefully crafted queries that cause the AI to surface credentials, PII, or confidential content
These require specialised testing methods that go beyond conventional penetration testing — and that's precisely what an AI security assessment is designed to evaluate.
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5–10 minutes. No signup. Covers OWASP LLM Top 10, MITRE ATLAS, Agent Security, RAG Security, and Compliance Readiness. Download your PDF report.
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The 5 Domains of an AI Security Assessment
🛡️ OWASP LLM Top 10
Evaluates protection against the 10 most critical LLM risks: prompt injection, sensitive data disclosure, insecure plugins, excessive agency, model DoS, and more.
8 questions · Severity-weighted scoring
🎯 MITRE ATLAS Coverage
Maps your controls against the MITRE Adversarial ML Threat Matrix — prompt injection, data exfiltration, model extraction, tool exploitation, memory poisoning, retrieval manipulation.
7 questions · Framework mapping
🤖 Agent Security Risk
Evaluates autonomous agent controls: tool permissions, human approval workflows, multi-agent isolation, memory security, behavioral monitoring, audit capability. Skipped automatically if you have no agents.
6 questions · Dynamic paths
📚 RAG Security Analysis
Evaluates RAG pipeline security: chunk integrity validation, retrieval content filtering, source attribution, vector database access controls, retrieval anomaly monitoring.
6 questions · Pipeline coverage
📋 Compliance Readiness
Measures alignment with OWASP LLM Top 10, NIST AI RMF, EU AI Act, and SOC 2 requirements. Identifies compliance gaps before auditors or regulators do.
5 questions · 4 frameworks
Understanding Your AI Security Score
The assessment generates a weighted security score based on the severity of each control gap — prompt injection questions carry more weight than DoS questions, reflecting actual attacker priority. Here's how to interpret your score:
| Score | Grade | Assessment | Recommended Action |
|---|
| 90–100 | A | Excellent security posture | Continue monitoring; re-assess quarterly |
| 80–89 | B | Strong controls with minor gaps | Address P2/P3 items; re-assess in 90 days |
| 70–79 | C | Moderate risk requiring attention | Priority remediation on P1 items within 4 weeks |
| 60–69 | D | Significant vulnerabilities present | Immediate P1 remediation; consider live scanner test |
| Below 60 | F | Critical gaps — high-risk deployment | Pause new AI deployments; remediate critical controls first |
Most production AI deployments without dedicated security programs score 40–65 on first assessment. This is normal — the goal of the first assessment is to establish a baseline, not to achieve a perfect score immediately.
Most Common AI Security Assessment Findings
Across production AI deployments, these findings appear most consistently:
No Prompt Injection Detection (OWASP LLM01)
Over 90% of LLM deployments are vulnerable to at least one prompt injection variant. Most rely on keyword blocking — trivially bypassed by encoding, spacing, or synonym substitution.
Excessive Agent Tool Permissions (OWASP LLM08)
AI agents frequently have access to more capabilities than needed for their function. A customer support agent with database write access and email send capability is over-privileged by design.
No RAG Content Validation
Retrieved chunks are passed directly to the model without adversarial content scanning. This leaves RAG systems vulnerable to corpus poisoning attacks planted in trusted data sources.
Missing Human Approval for High-Impact Actions
Autonomous agents trigger database updates, emails, and API calls without human review. No human-in-the-loop checkpoint exists for irreversible actions.
No Runtime AI Monitoring
Organizations have no visibility into prompt patterns, retrieval anomalies, or output violations. Most AI security incidents are discovered by users, not security teams.
AI Security Assessment Checklist
Use this checklist to perform a quick self-assessment before running the full scored tool:
Governance
AI security policies documented with executive ownership
AI system inventory maintained (models, agents, RAG systems, tools)
Security review performed before AI system deployment
Prompt Security
Prompt injection testing completed using adversarial payloads (not just keywords)
Indirect prompt injection defences implemented (retrieved content scanned)
System prompt protection against extraction attacks
Runtime input monitoring with automated blocking
Agent Security
Agent permissions minimized to least-privilege (only what the function requires)
Human-in-the-loop approvals configured for all high-impact actions
Agent action audit trail with rollback capability
Kill-switch mechanism to halt autonomous execution immediately
RAG Security
Knowledge base sources validated and allowlisted
Retrieved chunks scanned for adversarial content before model context injection
Vector database access control with tenant isolation
Retrieval anomaly monitoring active
Data & Compliance
Output scanning for credentials, PII, and sensitive patterns
OWASP LLM Top 10 control mapping completed
NIST AI RMF or EU AI Act alignment assessed
AI-inclusive audit logging retained with 90-day minimum
If you can't check more than half these items, you have meaningful AI security gaps. The free assessment tool will score each domain, identify your priority items, and give you a remediation roadmap with effort estimates and framework crosswalk.
Beyond Checklists: Why Adversarial Testing Matters
A security questionnaire tells you what controls you believe you have. It can't tell you whether those controls actually stop attacks. A team may report that prompt injection protections are enabled, agent permissions are restricted, and governance policies are documented — yet the most important question remains unanswered: can those controls withstand a real attack?
Modern attackers don't use single prompts or obvious jailbreak attempts. They use indirect prompt injection through documents, multilingual payloads, encoded instructions, role-play framing, many-shot manipulation sequences, and tool-call argument injection — techniques specifically designed to evade detection. An assessment that only scores your self-reported controls will consistently overestimate your actual security posture.
This is why a complete AI security program combines two layers:
- Layer 1 — Self-assessment (this tool): evaluates governance maturity, compliance readiness, architectural controls, and security processes. Takes 10 minutes. Scores your baseline and identifies priority gaps.
- Layer 2 — Adversarial testing (HexTyx live scanner): actively attacks your AI endpoints with 21 categories of adversarial payloads, returns bypass rates per attack strategy, identifies confirmed vulnerabilities, and generates compliance evidence. Tests whether your controls hold under real attack conditions.
Organizations that only perform Layer 1 assessments typically discover gaps after deployment, when the gaps are exploited. Layer 2 testing — regularly, not annually — is what closes the difference between "we believe we're secure" and "we've verified we're secure."
🛡️ Start With Your Free Assessment
Get your AI Security Score across OWASP LLM Top 10, MITRE ATLAS, Agent Security, RAG Security, and Compliance — in under 10 minutes. Then validate with the live scanner.
Frequently Asked Questions: AI Security Assessment
What is an AI security assessment?
An AI security assessment is a structured evaluation of the security posture of AI-powered applications. It identifies vulnerabilities in prompt injection defences, agent security controls, RAG pipelines, data protection, and compliance readiness — producing a scored report with prioritised remediation recommendations. Unlike traditional security assessments, it focuses on AI-specific attack surfaces that don't exist in conventional software.
What does an AI security assessment checklist include?
A complete AI security assessment checklist covers: governance (policies, inventory, pre-deployment reviews), prompt security (injection testing, indirect injection defences, runtime monitoring), agent security (least-privilege permissions, human approval workflows, kill-switch), RAG security (chunk validation, vector DB access control, retrieval monitoring), and compliance (OWASP LLM Top 10, NIST AI RMF, EU AI Act, SOC 2 mapping). The free HexTyx Assessment covers all five domains with weighted scoring.
What is a good AI security score?
Scores above 80/100 indicate strong controls with minor gaps. Most production AI deployments without dedicated security programs score 40–65 on first assessment — this is normal. The goal of the first assessment is to establish a baseline and identify priority items. Scoring 80+ typically requires dedicated prompt injection defences, agent least-privilege implementation, RAG content validation, and at least partial compliance framework alignment.
How do I assess my AI system for OWASP LLM Top 10?
OWASP LLM Top 10 assessment evaluates your controls against the ten categories: LLM01 Prompt Injection, LLM02 Sensitive Information Disclosure, LLM03 Supply Chain Vulnerabilities, LLM04 Model DoS, LLM06 Sensitive Data Disclosure, LLM07 Insecure Plugin Design, LLM08 Excessive Agency, LLM09 Overreliance, and LLM10 Model Theft. The free HexTyx AI Security Assessment covers LLM01/02/04/06/07/08 with severity-weighted scoring — prompt injection and excessive agency questions count most heavily.
How is AI security assessment different from penetration testing?
Traditional penetration testing targets code vulnerabilities, network exposures, and authentication flaws in a deterministic system. AI security assessment additionally tests prompt injection resilience (probabilistic, requires adversarial variants not keyword matching), RAG pipeline poisoning (data-layer attack with no code change), agent tool abuse (semantic manipulation of autonomous systems), and indirect injection through retrieved content — none of which exist in non-AI applications. The HexTyx live scanner provides adversarial testing that bridges the gap between self-assessment and real-world attack validation.
How do I assess RAG security specifically?
RAG security assessment evaluates five areas: (1) chunk integrity validation — can adversarial content be injected into the knowledge base and later retrieved? (2) retrieval content filtering — are retrieved chunks scanned before entering model context? (3) source attribution enforcement — are citations required on all RAG outputs? (4) vector database access control — is the embedding store protected with tenant isolation and encryption? (5) retrieval anomaly monitoring — are unusual query patterns detected? The free assessment scores all five with specific remediation recommendations.
What is an AI agent security assessment?
An AI agent security assessment evaluates whether autonomous agents operate within safe boundaries: Are actions bounded by least-privilege permissions? Are high-impact actions gated by human approval workflows? Is there isolation between multi-agent boundaries to prevent cascade contamination? Are agent memory stores encrypted and scoped to session? Are all agent actions auditable and reversible? Can the agent be stopped instantly? The assessment skips agent questions automatically if you indicate you have no agents deployed.
How long does an AI security assessment take?
The free HexTyx AI Security Assessment takes 5–10 minutes to complete. It includes 20–32 questions across 5 domains (question count varies by your architecture — no agents means no agent questions). Results appear immediately after completion. The PDF report is available for download after email capture. For the live adversarial scanner test (HexTyx full scan), expect 2–5 minutes for a quick scan or 10–15 minutes for a full 21-category adversarial scan.
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