Starseer AI (@StarseerAI) / X

Starseer AI

109 posts

Starseer AI

@StarseerAI

Interpretable AI for Measurable Security

starseer.ai

Joined March 2025

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Followers

Starseer AI
@StarseerAI
Jun 8
For the first time in 19 years, vulnerability exploitation is the #1 breach entry point. Not credentials. Not phishing. Software flaws, hit within hours of disclosure because AI compressed the attack timeline. The 2026 Verizon DBIR analyzed 22,000+ breaches across 145 countries.
The 2026 Verizon DBIR Closed the Window. Your AI Endpoints Are Still Open.
From starseer.ai
23K
Starseer AI
@StarseerAI
Jun 1
Your EDR sees a node process making API calls. The process tree ends there. The prompts, model invocations, security evaluations, policy decisions: all invisible. Prompt lineage is the process tree for AI. Every request, model call, and policy decision in a single trace from
Prompt Lineage: The Process Tree for the AI Era
From starseer.ai
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Starseer AI
@StarseerAI
May 22
We beat full fine-tuning with 31% fewer layers. On GSM8K math reasoning, full LoRA across all 32 layers of Llama-3.1-8B produced: → 59.2% exact match → 34.8% answer found Starseer's combined interpretability signal, using 22 layers: → 60.7% exact match (+103%) → 58.6%
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Starseer AI
@StarseerAI
May 19
3 layers. 17 models. 7 architectures. 85%+ accuracy. We tested jailbreak detection using just 3 layers selected by activation patterns, across every major open-source model family: Mistral, Llama, Qwen, Olmo, and others. Models ranged from 0.5B to 32B parameters. The accuracy
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Starseer AI
@StarseerAI
May 18
"No Security Meter for AI" from BIML is the most important AI security paper this year. Benchmarks don't measure security. Output monitoring misses threats by design. The only way forward is getting inside the model. Starseer was built on that thesis. BIML's independent
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Starseer AI
@StarseerAI
May 15
Your guardrail is slower than your model. Most AI safety stacks run a separate 7–9B parameter guard model alongside production inference. ShieldGemma-9B adds 570ms. WildGuard-7B adds 106ms. Every request, every time. Starseer's interpretability-based probe runs in ~38ms. Same
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Starseer AI
@StarseerAI
May 13
7B parameters. 570ms latency. That's what guardrails cost today. Starseer: ~38ms. ~1,000x fewer parameters. 96.3% accuracy. Same job. Different approach. #AI #Guardrails #Interpretability
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Starseer AI
@StarseerAI
May 12
You're running 7 billion parameters to do what 3 layers can handle. That's the finding from our latest research at Starseer. We used interpretability signals to look inside neural networks and identify exactly which layers, neurons, and activation patterns drive specific
Stop Running Entire Models When You Only Need a Few Layers
From starseer.ai
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Starseer AI
@StarseerAI
May 11
How exposed is your AI stack? It's the question every security leader is being asked right now, and most don't have a clean way to answer it. Shadow AI is everywhere. Governance policies are half-drafted. Agents are running in production before anyone has audited their
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Starseer AI
@StarseerAI
May 9
"Evidence, not inference." Here's what AI security looks like when it's built on interpretability instead of guardrails alone: > Before deployment, AI-Verify examines models against approved baselines, known safe models. > At runtime, AI-DE engineers detection logic grounded in
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Starseer AI
@StarseerAI
May 7
"Detection that traces the decision, not just the output." Validating a model before deployment is critical. But models don't operate in a vacuum. They encounter new data. They drift. Agents make autonomous decisions across multi-step chains. The conditions of production are
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Starseer AI
@StarseerAI
May 5
Your engineering team is burning $2,000/month per developer on AI tokens. Are you sure every one of those tokens is going to the right model? Here's what we're seeing across the industry: → Uber exhausted its full-year AI budget by April → A 4-person startup spent $113K in a
A $7 Million Problem: Why Every AI Request Needs an Intelligent Router
From starseer.ai
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Starseer AI
@StarseerAI
May 4
"Know what's inside before it ships." You wouldn't deploy software without reviewing the source code for vulnerabilities. So why do organizations deploy AI models after testing only their outputs? Behavioral evaluation, running test prompts, checking for toxic outputs,
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