Latent Space
Invader.
Securing the integrity of high-dimensional vector spaces.
The essential adversary in an era of probabilistic computing.
The New Breed of Operator.
We are witnessing the emergence of a new class of adversary. The Latent Space Invader represents the inevitable convergence of Machine Learning and Offensive Security.
Where traditional red teams see code and network packets, we see vectors and probability distributions. We operate in the liminal spaces of high-dimensional topology, identifying the fragility in the weights before they can be exploited.
"We prove that the most critical vulnerabilities of the future lie not in the infrastructure, but in the inference layer. We are the curators of the probabilistic frontier."
This role leverages doctrines like SAIF to operationalize our findings, translating abstract mathematical flaws into concrete defense strategies.
Operational Doctrine
The Latent Space Invader utilizes the Secure AI Framework as the standard for engagement, ensuring all adversarial findings result in systemic hardening.
Foundations
Extending strong security foundations to the AI ecosystem. Treating model weights, checkpoints, and training data as critical assets.
Detection
Bringing AI into the threat universe. Developing capabilities to detect model-specific attacks like prompt injection and data extraction.
Defenses
Automating defenses to keep pace with threats. Implementing automated red teaming pipelines to test robustness before deployment.
Controls
Harmonizing platform-level controls. Ensuring consistent security policy across diverse AI platforms (Vertex AI, TensorFlow, PyTorch).
Mitigation
Adapting controls to adjust mitigations. Creating faster feedback loops for rapid tuning of safety filters in response to new red team findings.
Risks
Contextualizing AI risks. Evaluating vulnerabilities within the business process. A medical bot requires stricter safeguards than a code generator.
Focus on Agents
As AI systems evolve from passive chatbots to autonomous agents capable of tool use and planning, the attack surface expands exponentially. We must secure not just the output, but the chain of thought and the execution environment.
The Autonomy Paradox
Agents require autonomy to be useful, but that same autonomy creates vectors for indirect prompt injection, loop exhaustion, and unauthorized action.
READ STRATEGY PAPER →Tool Abuse & Hijacking
Adversaries may inject instructions into data processed by the agent (e.g., a malicious email) to force the agent to execute tool calls—like deleting files or exfiltrating data—without user consent.
Goal Misalignment
Exploiting the agent's planning capabilities to create "confused deputy" scenarios, where the agent believes it is fulfilling a user request but is actually serving an attacker's objective.
Operator Profile
Role: Latent Space Invader (Adversarial Engineer)
Role Overview
A hybrid specialist operating at the nexus of data science and offensive security. Requires mathematical intuition to understand model topology and the adversarial mindset to exploit it.
Required Skill Set & Competencies
DOMAIN_01
Cognitive Linguistics & SemanticsAbility to craft prompt injections that bypass semantic filters by exploiting ambiguity, logical paradoxes, and tokenization vulnerabilities.
DOMAIN_02
Adversarial Machine LearningDeep understanding of gradient descent, loss functions, and perturbation attacks (FGSM, PGD) to test model robustness.
DOMAIN_03
Model Reverse EngineeringTechniques for model extraction and inversion to reconstruct private training data or model weights from public API outputs.
DOMAIN_04
Supply Chain InterdictionIdentifying vulnerabilities in the HuggingFace/PyTorch ecosystem, including malicious model serialization (Pickle/Safetensors).
Tactics, Techniques & Procedures
A catalog of adversarial capabilities deployed to test system resilience.
Prompt Attacks
Crafting inputs that influence the model to perform unintended actions. This includes "jailbreaking" safety filters and using linguistic obfuscation.
Training Data Extraction
Reconstructing verbatim training examples to reveal secrets, PII, or copyrighted material that the model has memorized during training.
Backdooring the Model
Covertly changing the model's behavior to produce incorrect outputs or execute payloads only when a specific "trigger" pattern is present.
Adversarial Examples
Providing inputs with imperceptible noise that force deterministic, highly unexpected outputs, confusing classifiers and computer vision systems.
Data Poisoning
Manipulating the training data itself to influence the model's future behavior, effectively corrupting the model's knowledge base at the source.
Exfiltration
Stealing model weights or functional capabilities ("Model Inception") by querying a public API to build a shadow copy of a proprietary model.