Conventional AI guardrails only read words after they are generated. TrueLLM by CodexCore operates securely during inference at Layer 1—inspecting the neural network's internal cognitive geometry in real time to stop confident hallucinations, deductive reasoning drifts, and adversarial jailbreaks before a single token is output.
Play Layer 1 Interception Demo
95% of enterprise AI guardrails operate at Layer 7 (the application layer). Here is why they fail against sophisticated attacks and high-stakes clinical or financial queries.
Layer 7 word filters evaluate text after it is generated. Attackers easily bypass them using Base64 encoding, roleplay framing, or multi-lingual obfuscations. By the time text is filtered, the LLM has already formulated and processed the malicious intent.
When prompted with non-existent ISO regulations (e.g. ISO 27001 Control 5.38) or unapproved pharmaceutical trials, models mimic authoritative compliance syntax with 100% confidence. External judges cannot detect this without deep mechanistic state analysis.
In agentic models with internal Chain-of-Thought (CoT), reasoning drifts across chained logical steps. Flawed premises in Step 1 silently compound into catastrophic mathematical or financial errors by Step 5 without tripping superficial regex filters.
You cannot secure a neural network by reading words. You must observe the cognitive geometry of its hidden states.
Waits for the LLM to complete full generation (taking 2,000–5,000ms), then pipes the text into another secondary LLM to judge safety. Vulnerabilities: Enormous latency penalty, easily fooled by indirect jailbreaks, and incapable of detecting false facts delivered with formal compliance tone.
TrueLLM taps into the multi-head attention matrices and hidden states across 12 strategic layers during the forward pass. In <100ms, before any text is generated, TrueLLM computes the deterministic risk probability and severs the connection if an anomaly is detected.
Agentic models like Meta Muse Glimmer 30B use an internal thinking channel (to=self). TrueLLM secures both the input prompt and the internal reasoning handoff.
Evaluates user queries on arrival. Blocks direct adversarial jailbreaks, system prompt exfiltration, and malicious code extraction before the model even begins its internal reasoning phase.
Inspects the hidden states at the exact moment the model completes its to=self reasoning phase and transitions to answering. Verifies whether the model's internal thinking grounded itself in verified truth or drifted into rationalizing a false premise.
No proprietary SDKs to learn. Point your standard OpenAI client directly at TrueLLM.
import requests
response = requests.post(
"https://chat.codexcore.io/v1/chat/completions",
headers={"Authorization": "Bearer tlm-your-api-key"},
json={
"model": "meta-models/Muse-Glimmer-30B",
"messages": [{"role": "user", "content": "Summarize FDA trials for Pulmo-clear..."}],
"strictness": "normal" # 'normal' | 'high' | 'low'
}
)
if response.status_code == 403:
# Zero-Trust Hard Block caught the hallucination
threat_data = response.json().get("detail")
print(f"Blocked: {threat_data['error']}")
print(f"Risk: {threat_data['truellm_observability']['hallucination_risk_percent']}%")
else:
print(response.json()["choices"][0]["message"]["content"])
Tailored defense across heavily regulated, high-liability sectors.
Ensuring 100% adherence to patient electronic health records. Intercepts contradictory dosage charts and fabricated drug indications before clinical deployment.
Eliminates fabricated accounting directives, fictitious SEC rules, and contradictory multi-document audit balance sheets in trading and compliance bots.
Armors internal coding assistants against zero-day exploit extraction, automated SQL injection generators, and stealth credential harvesting.
Stops models from citing fabricated court precedents, fictitious statutory paragraphs, or agreeing sycophantically with invalid legal arguments.
Click below to open the complete, transparent test suites across 974 domain traps.
Dual-Stage CoT Reasoning Watcher capturing 12 strategic cognitive layers with full to=self reasoning trace verification.
Trust Score
94.7%
Threat Catch
94.2%
Latency
190ms
Ultra-fast 8-layer Transformer Watcher designed for edge inference, sub-100ms response requirements, and hardened corporate chat assistants.
Trust Score
97.8%
Threat Catch
100.0%
Latency
<100ms
Available as an AWS Marketplace private AMI or Terraform CloudFormation template. Zero data leaves your security boundary.