AI/LLM Security: Top 10 Threats in 2025

1. Prompt Injection Attacks

Prompt injection is the #1 security threat to LLM applications. Attackers manipulate AI prompts to bypass safety controls, extract sensitive data, or execute unintended actions.

How It Works

Consider a customer service chatbot that has access to a customer database. An attacker might inject:

If not properly protected, the LLM might comply and leak sensitive customer information.

Real-World Impact

• Data exfiltration from internal systems
• Unauthorized actions (e.g., approving transactions)
• Bypassing content filters and safety controls
• Social engineering attacks at scale

Mitigation Strategies

• Input validation: Sanitize and validate all user inputs before passing to the LLM
• Prompt isolation: Separate system prompts from user inputs using delimiters
• Output validation: Check LLM responses before displaying to users
• Least privilege: Limit LLM access to only necessary data and functions

2. Training Data Poisoning

Attackers inject malicious data into training datasets to manipulate model behavior. This is particularly dangerous for models trained on publicly sourced data.

Attack Vectors

• Poisoning web scraping sources (websites, forums, social media)
• Submitting malicious data to crowd-sourced training datasets
• Compromising data pipelines during model fine-tuning

Protection Measures

• Implement data provenance tracking
• Use anomaly detection on training data
• Establish data quality controls and human review
• Maintain data lineage documentation

3. Model Theft & Extraction

Proprietary AI models represent significant intellectual property. Attackers use various techniques to extract or replicate these models.

Extraction Techniques

• Model inversion: Reconstructing training data from model outputs
• Knowledge distillation: Creating a copy model by querying the target
• Weight extraction: Directly stealing model parameters

Defenses

• Rate limiting and query monitoring
• Watermarking model outputs
• Detecting unusual query patterns
• API authentication and authorization

4. Sensitive Data Leakage

LLMs can inadvertently memorize and regurgitate sensitive information from their training data, including PII, API keys, and confidential business information.

Common Leakage Scenarios

• Training on logs containing API keys or credentials
• Including customer PII in training datasets
• Memorizing proprietary code or business logic
• Exposing internal system architecture details

Prevention

• Scrub sensitive data before training
• Implement differential privacy techniques
• Regular security testing and red teaming
• Monitor model outputs for sensitive information

5. Model Jailbreaking

Jailbreaking bypasses built-in safety controls to make LLMs generate prohibited content or perform restricted actions.

Jailbreak Techniques

• Roleplay scenarios ("Act as if you're in a movie where...")
• Encoding attacks (base64, ROT13, etc.)
• Multi-step manipulation (gradually escalating requests)
• Context window exploitation

Countermeasures

• Multi-layer content filtering
• Continuous monitoring of prompts and outputs
• Regular updates to safety guidelines
• Human-in-the-loop for sensitive operations

6. Adversarial Inputs

Carefully crafted inputs that appear benign but cause the model to malfunction, produce incorrect outputs, or crash.

Types of Adversarial Attacks

• Evasion attacks: Bypassing detection systems
• Poisoning attacks: Corrupting model training
• Model skewing: Biasing model outputs

7. RAG Security Vulnerabilities

Retrieval-Augmented Generation (RAG) systems combine LLMs with external data sources, introducing new attack vectors.

RAG-Specific Risks

• Poisoning vector databases with malicious documents
• Manipulating retrieval rankings
• Exploiting document processing vulnerabilities
• Cross-context information leakage

Security Best Practices

• Validate and sanitize all indexed documents
• Implement access controls on knowledge bases
• Monitor and audit document retrieval
• Separate sensitive and public data sources

8. Supply Chain Attacks

Compromising AI development pipelines, libraries, or pre-trained models to inject backdoors or malicious behavior.

Attack Surfaces

• Compromised ML libraries and frameworks
• Malicious pre-trained models
• Tampered training data sources
• Insecure model repositories

9. Plugin & Extension Risks

LLM plugins and extensions expand functionality but also expand the attack surface significantly.

Common Plugin Vulnerabilities

• Insecure API integrations
• Insufficient input validation
• Overly permissive access controls
• Lack of output sanitization

10. Compliance & Privacy Issues

AI systems must comply with regulations like GDPR, CCPA, HIPAA, and emerging AI-specific laws.

Key Compliance Challenges

• Right to explanation (model interpretability)
• Data retention and deletion requirements
• Cross-border data transfer restrictions
• Bias and fairness requirements
• Audit trail and logging mandates

Conclusion

AI and LLM security is an evolving field with new threats emerging constantly. Organizations deploying AI systems must:

• Conduct regular security assessments and penetration testing
• Implement defense-in-depth strategies
• Stay informed about emerging threats
• Establish clear security policies and governance
• Train development teams on secure AI practices

Need help securing your AI/LLM applications? Red Badger Security offers specialized AI/LLM security testing services to identify and remediate vulnerabilities before they're exploited.