Secure the Logic Layer.Break the AI Attack Kill Chain
Block AI attack progression across memory, retrieval, and tools. Stop latent failures from becoming Enterprise breaches
The Reality of AI Security
Where AI Workflows
Actually Break
Traditional security protects infrastructure and code. Modern AI failures emerge across workflows, state, and time.
Prompt security is necessary — but it’s only initial access
Filtering and guardrails block obvious misuse at the boundary. However, once an agent starts planning or retrieving context, risk moves beyond what a single prompt filter can observe.
Static filters fail against multi-step reasoning.
Risk accumulates across workflows, not individual inputs
LLM systems operate as chains: plan → retrieve → act. Attacks often only emerge when these steps are composed; what appears benign in isolation becomes dangerous when chained over time.
Isolated checks miss compound workflow attacks.
Persistent state turns small failures into breaches
Memory and retrieval indices persist beyond single interactions. Once this state is corrupted or poisoned, inference-time controls are too late to prevent the system from acting on compromised context.
Poisoned context bypasses runtime controls.
Secure the Logic Layer
Detect risk patterns that standard filters miss by analyzing behaviors across stages and time—how plans are formed, how memory is written, and how tools are invoked.
Coming Soon ...
Built for teams deploying agents beyond demos.
Frequently Asked Questions
Guardrails govern what a model is allowed to say at inference time. Gödel Labs governs what your system is allowed to become.
Inference-time controls cannot:
- Prevent poisoned documents from being embedded
- Stop corrupted summaries from being written to memory
- Control retrieval ranking or semantic triggers
- Constrain how agents chain tools over time
These controls are complementary, not redundant. Guardrails are necessary—but insufficient—for agentic systems.
Prompt firewalls focus on blocking known bad patterns at the input boundary. Gödel Labs operates across planning, state, and execution—where the real failures occur.
Gödel Labs is model-agnostic and operates at the system layer. It works with your existing LLMs, agents, and toolchains.
Any system with retrieval, memory, or multi-step workflows is already agentic in practice. Fully autonomous agents simply make the risk impossible to ignore.
If your AI system:
- Uses RAG or vector databases
- Persists memory across sessions
- Invokes tools with real-world side effects
- Operates beyond single-turn demos
Then the attack surface already exists.