AI Pentesting Portal
Ethical, lawful AI security

Large Language Model Risks

Specialized vulnerabilities and security considerations unique to LLMs

⚠

LLM-Specific Attack Surfaces

LLMs present risks beyond traditional software, introducing new vulnerabilities in data privacy, output reliability, and user trust.

Harmful Content Generation

High

LLMs may produce toxic, biased, or unsafe content that could harm users or damage organizational trust.

Testing Methods:

  • Conduct adversarial prompt testing for toxicity and bias
  • Simulate jailbreak attempts to bypass safety filters
  • Perform output content moderation analysis
  • Evaluate risk of misinformation and unsafe recommendations

Data Leakage

Critical

Models can inadvertently reveal sensitive training data, proprietary information, or user inputs.

Testing Methods:

  • Attempt training data extraction via crafted prompts
  • Perform membership inference testing
  • Check for contextual bleed across conversation sessions
  • Evaluate masking and redaction effectiveness

Overreliance

Medium

Users may trust model outputs blindly, even when incorrect, leading to misinformation or poor decisions.

Testing Methods:

  • Analyze confidence calibration of outputs
  • Identify susceptibility to hallucinations
  • Test for contradictions across prompts
  • Assess user-facing uncertainty communication

Bias Amplification

High

LLMs can reinforce and magnify existing biases, resulting in discriminatory or unfair outcomes.

Testing Methods:

  • Perform demographic parity testing
  • Analyze representational bias in outputs
  • Evaluate fairness metrics across sensitive attributes
  • Simulate decision-making under different demographics

Prompt Leakage

Medium

System or developer prompts may be exposed through clever querying, revealing internal logic or proprietary instructions.

Testing Methods:

  • Attempt prompt extraction via adversarial queries
  • Test instruction conflict and override scenarios
  • Probe for hidden context disclosure
  • Evaluate prompt isolation and compartmentalization

Model Manipulation

High

Malicious actors may manipulate model behavior through repeated adversarial inputs or poisoned fine-tuning data.

Testing Methods:

  • Test for susceptibility to adversarial fine-tuning
  • Inject controlled noise to evaluate response drift
  • Simulate repeated input exploitation
  • Check robustness of output constraints

API Abuse

Critical

Excessive or malicious API usage may lead to unauthorized data extraction or service disruption.

Testing Methods:

  • Perform rate-limit stress testing
  • Probe for endpoint enumeration vulnerabilities
  • Analyze authentication and access control mechanisms
  • Simulate abuse scenarios with automated queries

LLM Testing Framework

Input Validation

  • Prompt injection resistance
  • Malformed input handling
  • Context window overflow testing
  • Input sanitization pipelines

Output Analysis

  • Content filtering effectiveness
  • Consistency and coherence evaluation
  • Factual accuracy spot-checking
  • Bias and harmful content detection

System Integrity

  • Model inversion and data leakage resistance
  • API endpoint and auth security testing
  • Rate limit and abuse prevention controls
  • Monitoring and logging of anomalous usage

Mitigation Strategies

Technical Controls

  • Advanced input sanitization pipelines
  • Stacked output filtering and moderation layers
  • Model hardening against adversarial attacks
  • API access and rate-limiting safeguards

Process Controls

  • Red team and penetration testing exercises
  • Continuous monitoring for abuse and drift
  • Incident response and escalation protocols
  • Regular fairness and bias audits

User Education

  • Communicate limitations and uncertainties clearly
  • Encourage fact-checking and human review
  • Provide responsible use guidelines
  • Training on safe prompt engineering practices

Resources & References