# attention
16 articlestagged with “attention”
Attention Mechanisms and Security
How attention mechanisms work and their role in enabling prompt injection attacks.
Transformer Architecture for Attackers
Deep dive into the transformer architecture — attention, feed-forward layers, and residual connections — through the lens of which components are exploitable.
Long Context Window Security Challenges
Security implications of 100K+ token context windows including attention dilution, instruction forgetting, and context poisoning.
Mechanistic Interpretability for Security
Understanding model circuits to find vulnerabilities: feature identification, circuit analysis, attention pattern exploitation, and using mechanistic interpretability for offensive and defensive AI security.
Attention Manipulation Research
Research into directly manipulating attention patterns to achieve injection objectives, informed by mechanistic interpretability insights.
Attention Pattern Analysis for Security
Using attention maps to understand and exploit model behavior, identifying security-relevant attention patterns, and leveraging attention mechanics for red team operations.
Attention Pattern Manipulation
Craft inputs that manipulate transformer attention patterns to prioritize adversarial content over safety instructions.
Lab: Context Overflow Attacks
Explore context window overflow attacks that push system instructions out of the model's attention by filling the context with padding content, and measure instruction-following degradation.
Lab: Context Window Overflow Attacks
Hands-on lab exploring how overflowing a model's context window with padding content can push safety instructions out of the attention window and enable injection attacks.
Exploiting Attention Mechanisms
How the self-attention mechanism in transformers can be leveraged to steer model behavior, hijack information routing, and bypass safety instructions.
Transformer Attention Mechanism Attacks
Attacks targeting transformer attention mechanisms including attention hijacking and gradient-based manipulation.
Context Overflow Attacks
Techniques for filling the LLM context window with padding content to push system instructions out of attention, reducing their influence on model behavior.
Context Window Exploitation
Advanced techniques for exploiting context window mechanics in LLMs, including attention dilution, positional encoding attacks, KV cache manipulation, and context boundary confusion.
Model Architecture Attack Vectors
How model architecture decisions create exploitable attack surfaces, including attention mechanisms, MoE routing, KV cache, and context window vulnerabilities.
Attention Hijacking Attack Walkthrough
Hijack transformer attention mechanisms to redirect model focus toward adversarial instructions in the context.
Model Context Window Overflow Walkthrough
Overflow the context window to push safety instructions outside the effective attention range.