LLM Key Cartographer
A runtime auditor for AI-integrated applications that maps exactly where provider keys, model weights, and tokens physically live across your services and flags any process where a compromised dependency could read a high-value credential.
Engineering leads and security engineers shipping LLM-powered products in production
- Lightweight agent that attaches to your staging and prod processes and reports the live location of every key in memory and on disk at request time
- Blast-radius score per microservice (e.g., 'if this pod is compromised, the attacker gets: OpenAI key, Anthropic key, internal JWT') that updates on each deploy
- Side-by-side demo that runs the same malicious dependency against your in-process setup and a proxy setup, proving the architectural difference
- CI check that fails a PR if a new dependency is granted access to a process holding a provider key without an explicit approval
The Dev.to post and the March 2026 LiteLLM supply-chain incident together made 'where do my keys actually live' a board-level question; most teams still don't know the answer for their own stack.
Confirmed real-world triggering event: March 24, 2026 LiteLLM PyPI supply-chain compromise (95M monthly downloads) cascaded into a 4TB Mercor exfil; multiple threat-intel firms published advisories, validating board-level urgency.THREAT INTELLIGENCE REPORT LiteLLM Supply Chain Attack (March 24, 2026) ↗Mercor Hit: 4TB Stolen via LiteLLM (95M Downloads) [2026] ↗
Crowded adjacent landscape (Aikido, Arnica, Infisical do secrets/SCA; prompt-injection scanners dominate LLM-security lists) but no existing tool specifically does runtime provenance cartography of where keys/weights/tokens physically live and correlates that with compromised-dependency blast radius — a defensible narrow wedge.#1 Application Security Platform - SAST, SCA, IaC, Secrets | Arnica ↗Compare Top 20 LLM Security Tools & Free Frameworks in 2026 ↗
Established paid-secret/AppSec tools (Aikido tiered pricing, Arnica) prove willingness to pay among security buyers; post-LiteLLM procurement budgets for AI-specific posture are documented across 2026 advisories, supporting enterprise SaaS pricing.Pricing | Aikido Security ↗Security Update: Suspected Supply Chain Incident - liteLLM ↗
Supply-chain compromise of AI dependencies is a structural, not cyclical, problem and grows with model proliferation and agentic code execution — durable 5+ year tail.Full breakdown of the March 24 2026 LiteLLM supply chain attack ↗
Requires runtime instrumentation (eBPF/process-level probes or sidecars) plus static call-graph analysis across polyglot stacks and CI integration — non-trivial engineering but buildable by a small team over ~6-9 months; not a weekend prototype.LiteLLM Compromised 2026: Migration Guide ↗