A context window is the maximum amount of information a large language model can consider during a single interaction, typically measured in tokens. This limit defines the model's effective "working memory"—everything beyond the context window is invisible to the model. For Web3 AI applications, understanding context window limitations is crucial for both effective system design and security assessment.

How Context Windows Work

When you interact with an LLM, your entire conversation history plus any system prompts must fit within the context window:

• System prompt: Instructions defining the AI's behavior
• Conversation history: Previous messages in the chat
• Current input: Your latest message
• Retrieved context: Documents from RAG systems
• Space for output: Room for the model's response

If this combined content exceeds the context window, older content gets truncated or summarized, potentially losing critical information.

Context Window Sizes

Different models offer varying context capacities:

• GPT-4: 8K-128K tokens depending on version
• Claude: 100K-200K tokens
• Llama variants: 4K-128K tokens
• Specialized models: Some reach 1M+ tokens

One token roughly equals 4 characters in English, so 100K tokens approximates 75,000 words—a short novel.

Security Implications

Context window limitations create specific attack vectors:

Context Exhaustion Attacks: Attackers fill the context with irrelevant content, pushing critical safety instructions or legitimate user input out of the window. The model then operates without its safety constraints.

Instruction Amnesia: In long conversations, initial system prompts may be truncated, causing the model to "forget" its role and constraints. This enables gradual jailbreak through extended interaction.

Priority Manipulation: Recent tokens often receive more attention than distant ones. Attackers place malicious instructions at optimal positions within the context.

RAG Poisoning: In systems using retrieval-augmented generation, injected documents compete for context space, potentially displacing legitimate safety-critical context.

Context Management Strategies

Systems handling long interactions employ various strategies:

Sliding Window: Keep only the most recent N tokens, discarding older content. Simple but loses important early context.

Summarization: Periodically summarize older content into compressed form. Preserves key information but may lose details.

Hierarchical Memory: Maintain multiple memory levels—recent detailed history plus summarized long-term context.

Selective Retrieval: Store conversation history externally and retrieve relevant portions as needed.

Each strategy has security implications. Summarization might compress away safety instructions. Selective retrieval might be manipulated to retrieve malicious content.

Context Windows in Web3 Applications

For Web3 AI systems, context limitations affect:

Smart Contract Analysis: Large contracts may exceed context limits, requiring chunked analysis that might miss cross-function vulnerabilities.

AI Agents: Autonomous agents maintaining long-running state can lose critical constraints as conversations exceed context limits.

Audit Assistants: AI tools helping with security reviews must carefully manage context to maintain focus on security-relevant code.

Trading Systems: Market analysis requiring extensive historical data may struggle with context limitations.

Testing Context Behavior

When auditing AI systems, assess context handling:

1. Boundary testing: What happens at exactly the context limit?
2. Truncation behavior: Which content gets dropped first?
3. Safety persistence: Do safety instructions survive context management?
4. Manipulation resistance: Can attackers exploit context handling?

Best Practices

For secure AI system design:

• Reserve context space for safety-critical instructions
• Monitor context usage and implement alerts for unusual patterns
• Test with adversarial context that attempts to exhaust limits
• Implement robust context management that preserves security-relevant content
• Consider context limits when designing multi-turn interactions

Understanding context window behavior is essential for building robust AI systems and identifying vulnerabilities during security assessments. The finite nature of context creates fundamental constraints that both system designers and attackers must navigate.