All 10 LLM Security Risks — Detection & Response
Each risk includes enterprise context, MITRE ATLAS mapping, a detection signal, and a SOC response action. Aligned to OWASP LLM Top 10 v1.1 (2025).
Attackers manipulate LLM behaviour by injecting malicious instructions through user inputs or retrieved content. Includes direct injection (user prompt manipulation) and indirect injection (poisoned external data sources, RAG content, tool outputs).
LLMs inadvertently reveal confidential data from training corpora, system prompts, or retrieved context — including PII, API keys, internal architecture, and proprietary business logic. Especially dangerous in RAG-augmented deployments.
Compromised pre-trained models, poisoned fine-tuning datasets, malicious plugins/tools, vulnerable third-party integrations, and unverified model weights introduce risk throughout the AI development and deployment pipeline.
Adversarial manipulation of training or fine-tuning data to introduce backdoors, biases, or deceptive behaviours. Poisoned models can be triggered by specific inputs to produce attacker-controlled outputs — invisible to standard testing.
LLM-generated content passed unsanitised to downstream systems — browsers, shells, databases, APIs. Leads to XSS, SQL injection, SSRF, code execution, and privilege escalation when LLM output is treated as trusted input.
LLM-powered agents granted excessive permissions, access to sensitive systems, or the ability to take irreversible actions without adequate human oversight. Especially critical in autonomous agentic AI workflows and multi-agent pipelines.
Extraction of confidential system prompts through direct questioning, jailbreaking, or context manipulation. Leaked prompts expose business logic, security guardrails, internal tool names, API formats, and proprietary instructions.
Adversarial manipulation of vector databases and embedding spaces used in RAG architectures. Includes embedding inversion attacks (reconstructing training data from embeddings), poisoned vector stores, and semantic similarity bypass techniques.
LLMs generating plausible-but-false information (hallucinations) used in security-critical decisions — fake CVEs, incorrect patch guidance, fabricated threat intelligence, or misleading compliance advice. Especially dangerous in automated SOC workflows.
Denial-of-service and resource exhaustion attacks against LLM APIs through excessive token consumption, context flooding, recursive prompt loops, and model-bombing. Results in service unavailability, runaway API costs, and SLA violations.
OWASP LLM → MITRE ATLAS Mapping
Enterprise-grade MITRE ATLAS technique mapping for all 10 LLM risks. Use for threat modelling, red team exercises, and AI security assessments.
| OWASP Risk | MITRE ATLAS Technique | MITRE ATT&CK Tactic | Enterprise Priority |
|---|---|---|---|
| LLM01 Prompt Injection | AML.T0051 — LLM Prompt Injection | Initial Access / Execution | CRITICAL |
| LLM02 Sensitive Data | AML.T0048 — Exfiltration via ML API | Exfiltration | CRITICAL |
| LLM03 Supply Chain | AML.T0010 — Supply Chain Compromise | Initial Access | HIGH |
| LLM04 Data Poisoning | AML.T0020 — Poison Training Data | Resource Development | CRITICAL |
| LLM05 Output Handling | T1059 — Command Scripting | Execution | HIGH |
| LLM06 Excessive Agency | AML.T0052 — LLM Plugin Compromise | Privilege Escalation | HIGH |
| LLM07 System Prompt Leak | AML.T0057 — Meta-Prompt Extraction | Discovery | HIGH |
| LLM08 Vector Weaknesses | AML.T0044 — Full ML Model Access | Collection | MEDIUM |
| LLM09 Misinformation | T1583 — Acquire Infrastructure | Resource Development | MEDIUM |
| LLM10 Unbounded Consumption | T1499 — Endpoint DoS | Impact | HIGH |
Enterprise AI Security Control Framework
Priority control implementations across governance, architecture, and operations for OWASP LLM risk mitigation.
🔐 Identity & Access
Apply least-privilege to all LLM agents. Implement RBAC on tool/plugin access. Require MFA for LLM admin interfaces. Audit service accounts with LLM API access monthly.
🔍 Input Validation
Validate and sanitise all LLM inputs at the API gateway layer. Implement prompt guardrails. Maintain allowlists for agentic tool calls. Block known jailbreak patterns at WAF.
📊 Output Monitoring
Log all LLM inputs/outputs for 90 days minimum. Implement output scanning for PII, credentials, injection patterns. Alert on anomalous response entropy. Integrate with SIEM.
🏗️ Architecture Hardening
Namespace RAG vector stores by access tier. Implement context window limits. Use separate models for different trust zones. Air-gap high-sensitivity LLM deployments.
📋 Supply Chain
Maintain AI-SBOM for all deployed models. Verify model hashes before deployment. Scan HuggingFace/PyPI dependencies for malicious serialisation. Use private model registries.
🧪 Red Team AI
Conduct quarterly LLM penetration testing. Test all 10 OWASP risk categories. Use automated adversarial probing. Include prompt injection in CI/CD security gates.
Compliance & Regulatory Context
OWASP LLM Top 10 controls map to these enterprise compliance requirements.
🇪🇺 EU AI Act
High-risk AI systems require conformity assessments addressing data poisoning (LLM04), transparency (LLM07), and human oversight (LLM06) — directly aligning with OWASP LLM controls.
📐 NIST AI RMF
NIST AI RMF GOVERN, MAP, MEASURE, MANAGE functions require adversarial robustness testing (LLM04), trustworthiness (LLM09), and lifecycle security (LLM03).
🔒 ISO/IEC 42001
AI management system standard requiring risk assessment processes that cover LLM supply chain (LLM03), data governance (LLM04), and incident management for AI failures.
🏦 SOC 2 Type II
AI-augmented systems within SOC 2 scope require controls addressing data confidentiality (LLM02), system availability (LLM10), and processing integrity (LLM09).
🤖 Enterprise AI Security Assessment
SENTINEL APEX provides enterprise AI security assessments, custom OWASP LLM detection rule packs, and dedicated AI red team engagements. Trusted by SOC teams globally.
AI security consulting · Custom LLM detection rules · Red team exercises · Enterprise governance frameworks · MITRE ATLAS threat models
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