OWASP Top 10 for LLMs: A 2026 Update on the Most Critical Vulnerabilities
Why LLM Security Needs Its Own OWASP Moment
Large Language Models are no longer experimental tools. In 2026, they are embedded in enterprise search, customer support, software development, security automation, finance, and operations.
Yet many organizations still secure LLMs as if they were:
Simple APIs
Static applications
Isolated chatbots
They are not.
LLMs introduce new classes of vulnerabilities that do not fit neatly into traditional OWASP Web or API categories. Recognizing this, the security community has converged around an OWASP Top 10–style threat model for LLMs—focused on how these systems actually fail in production.
This post presents a 2026-updated OWASP Top 10 for LLMs, reflecting real-world enterprise deployments, agentic systems, and tool-connected models.
OWASP Top 10 for LLMs (2026 Edition)
1. Prompt Injection (Still #1, Still Underrated)
What it is:
Malicious input that overrides system instructions or alters model behavior.
Why it’s worse in 2026:
LLMs now control tools and agents
Prompt injection can trigger real actions, not just bad text
Enterprise impact:
Unauthorized data access
Policy bypass
Malicious tool execution
Mitigations:
Prompt boundary enforcement
Input sanitization
Instruction hierarchy validation
Zero-trust prompt handling
2. Indirect Prompt Injection via External Data
What it is:
Hidden instructions embedded in documents, emails, web pages, or data sources consumed by the LLM.
Why it matters now:
RAG pipelines and web-connected agents ingest untrusted content at scale.
Enterprise impact:
Silent instruction hijacking
Data exfiltration through responses
Manipulated agent behavior
Mitigations:
Content origin tracking
Instruction stripping
Context segmentation
Output validation
3. Training Data Poisoning
What it is:
Malicious or biased data influencing model behavior during training or fine-tuning.
2026 reality:
More enterprises fine-tune or continually retrain models
Feedback loops amplify poisoned data
Enterprise impact:
Systemic bias
Logic corruption
Long-term trust erosion
Mitigations:
Dataset provenance controls
Human review of training data
Statistical anomaly detection
Restricted retraining pipelines
4. Sensitive Data Exposure
What it is:
LLMs leaking PII, trade secrets, credentials, or regulated data through outputs or logs.
Why it persists:
Overly large context windows
Poor session isolation
Unredacted prompts
Enterprise impact:
Regulatory violations (GDPR, HIPAA, etc.)
IP loss
Legal liability
Mitigations:
Data classification and redaction
Context minimization
Output scanning
Strict logging controls
5. Insecure Tool and Plugin Integration
What it is:
LLMs misusing connected tools, APIs, or plugins.
Why this is critical in 2026:
LLMs are now execution engines, not just text generators.
Enterprise impact:
Unauthorized transactions
Data corruption
Privilege escalation
Mitigations:
Least-privilege tool access
Explicit allow-lists
Human-in-the-loop for high-risk actions
Full audit trails
6. Excessive Agency and Autonomy
What it is:
Granting LLMs too much decision-making or execution power.
New in 2026:
Agentic systems operate over long time horizons with minimal supervision.
Enterprise impact:
Unintended actions
Runaway processes
Hard-to-contain failures
Mitigations:
Autonomy boundaries
Kill switches
Step-level approvals
Time- and scope-limited agents
7. Model Hallucinations as a Security Risk
What it is:
Incorrect or fabricated outputs treated as truth.
Why this is now a vulnerability:
Hallucinations increasingly trigger downstream actions.
Enterprise impact:
Faulty decisions
Compliance violations
Automation failures
Mitigations:
Grounding with trusted sources
Confidence scoring
Output verification layers
Human review for critical paths
8. Inadequate Output Handling and Validation
What it is:
Blindly trusting LLM outputs in applications or workflows.
2026 concern:
Outputs now feed directly into code, configs, emails, and decisions.
Enterprise impact:
Injection attacks
Malformed actions
Cascading system errors
Mitigations:
Schema validation
Policy enforcement
Escaping and sanitization
Structured output formats
9. Lack of Observability and Monitoring
What it is:
Insufficient visibility into prompts, outputs, decisions, and failures.
Why it’s dangerous:
You can’t secure what you can’t observe.
Enterprise impact:
Undetected abuse
Delayed incident response
Compliance blind spots
Mitigations:
Full interaction logging
Behavior anomaly detection
Drift monitoring
Security alerting for AI events
10. Supply Chain and Model Dependency Risks
What it is:
Over-reliance on opaque third-party models and providers.
2026 risk landscape:
Rapid model updates
Changing policies
Vendor lock-in
Enterprise impact:
Sudden behavior changes
Compliance exposure
Loss of control
Mitigations:
Model risk assessments
Version pinning
Fallback models
Clear contractual safeguards
What’s Changed Since Early LLM Security Lists?
Compared to earlier years:
Agency and tool use are now top-tier risks
Behavior over time matters more than single responses
Governance and observability are security controls, not bureaucracy
LLM security is no longer just an AppSec problem—it’s systemic risk management.
How Enterprises Should Use This OWASP List
This list should:
Guide threat modeling for AI systems
Inform secure architecture design
Shape red teaming and testing
Influence procurement and vendor review
It should not:
Be treated as a checkbox exercise
Replace continuous monitoring
Ignore business context
To Conclude
LLMs are becoming decision-makers, operators, and agents inside enterprise systems. The attack surface has shifted from pages and endpoints to prompts, data, tools, and behavior.
The OWASP Top 10 for LLMs is not about fear—it’s about clarity.
Organizations that internalize these risks early will:
Deploy AI faster
Avoid costly incidents
Build trust with users and regulators
In 2026, secure AI is not a competitive advantage—it is the baseline.
