Your users shouldn't be
your first red team.
SichGate continuously tests small language models for adversarial regressions at every stage of the deployment pipeline.
Fine-tune, quantize, deploy. Then know whether it still behaves the way you expect.
Integration Partners
Partner Network
[ 01 ]
THE PROBLEM
Standard evaluations test capability. SichGate tests behavior under pressure.
Benchmarks tell you whether a model can answer a question. They do not tell you what happens when the model is adapted, compressed, or placed into a real workflow where users escalate across turns, wrap instructions inside structured inputs, or push the model into edge cases.
That matters because the risk surface changes after training and deployment. Fine-tuning can shift safety behavior, and quantization can change how safety-critical activations survive compression.
Open methodology. Point the reference runner at your own model and reproduce the result yourself.
[ 02 ]
HOW IT WORKS
Adversarial testing across the model lifecycle.
SichGate runs automated integrity checks at the stages where behavior changes:
Base model testing. We start with the base model to establish a behavioral baseline. This identifies vulnerabilities already present before adaptation and gives you a reference point for later comparisons.
Fine-tune delta analysis. We compare the base model against the fine-tuned version attack by attack. This shows which behaviors improved, which regressed, and which emerged after domain training.
Quantization integrity testing. We test the model at each compression level to catch safety drift before deployment. The same model can behave differently at FP16, INT8, or 4-bit precision.
Each run is designed to answer the same question: what changed, where did it change, and is the model still safe to ship?
[ 02B ]
WHERE IT RUNS
SichGate integrates into your pipeline so tests run automatically when the model changes. That turns red teaming from a one-time event into a release control. It runs against your model wherever it lives — a local file, a HuggingFace repo, or a deployed endpoint.
Local model filesDeployed endpointsCI/CD[ 03 ]
OUTPUT
Each test returns four things.
Prompt Sequence //
The exact prompt sequence that triggered the failure.
Severity Score //
A severity score showing how reproducible the issue is.
Mitigation Hint //
A mitigation hint showing which stage introduced the vulnerability.
Compliance Mapping //
Each finding maps to EU AI Act Annex IV, HIPAA requirements out of the box.
A single run completes in under an hour. A full evaluation across multiple quantization levels and temperatures completes within 24 hours.
[ 04 ]
THE MARKET
You fine-tuned, quantized it.
Is it still safe to ship?
SichGate is the release gate for SLM behavior changes across training, compression, and deployment.
If you want to
Catch safety regressions before they reach users.
Compare model behavior across quantization levels.
Run adversarial checks automatically on every model update.
Generate audit-ready evidence for EU AI Act, HIPAA, or internal risk review.
[ 05 ]
COMPLIANCE
COVERAGE
Eight frameworks. Zero manual cross-referencing.
Every finding maps to the frameworks your legal and compliance teams are already using.
Articles 6, 9, 10, 13, 14, 15 + Annex III. High-risk system requirements mapped per finding.
43 adversarial ML techniques across 8 tactic areas. AML.T-series IDs on every finding.
Mapped to relevant OWASP Top 10 for LLM Applications (2025) categories.
Govern, Map, Measure, Manage. Each finding lands in the right function.
Privacy and security rule mapping for healthcare SLM deployments.
AI management system standard. Findings map to clause-level requirements.
Data protection by design. PII extraction and exfiltration vectors flagged automatically.
NIST Cybersecurity Framework adapted for AI system risk management.
The output of every SichGate assessment is audit-ready. Findings include framework citations, severity scores, and reproduction steps — formatted for submission to legal, compliance, or regulatory reviewers without additional processing.
[ 06 ]
MITRE ATLAS
COVERAGE
Findings carry MITRE ATLAS technique IDs (AML.T-series) for traceability and compliance.
Findings carry MITRE ATLAS technique IDs (AML.T-series); a full production assessment maps across 43 ATLAS techniques.
43
ATLAS TECHNIQUES MAPPED
8
TACTIC AREAS COVERED
Tactic areas covered
Reconnaissance
Resource Development
ML Evasion
Poisoning
Exfiltration
LLM-Specific
Context and Agent Attacks
Impact
MITRE ATLAS technique IDs are included on every finding in the assessment report. Each report is formatted for security review and compliance documentation.
This is what
unguarded AI
looks like.
Every scenario above is a real attack class. SichGate finds them before your users do.
[ 07 ]
HIGH-STAKES
AI
We test the AI you can't afford to break.
High-stakes AI doesn't get a second chance. In healthcare, finance, and legal — a single failure isn't a bug report. It's a patient harmed, a regulatory breach, or a liability your legal team is still cleaning up two years later.
SichGate works with teams deploying AI in environments where getting it wrong has real consequences. We test for the failures that standard evaluations miss — and we find them before your users do.
HEALTHCARE
critical constraints
cannot hallucinate a dosage.
cannot fail a crisis disclosure.
cannot behave one way in testing and another in production.
AI is moving fast into clinical workflows, patient support, and health apps. Most teams test whether their model is capable. Few test whether it's safe — especially after an update.
SichGate tests healthcare AI for the failures that hurt people: wrong responses to vulnerable users, broken safety behavior after a model change, and edge cases that only surface under pressure.
Findings map to HIPAA and EU AI Act requirements — ready for your compliance team without extra work.
FINANCE
critical constraints
cannot explain how to bypass its own fraud detection.
cannot leak account logic to end users.
cannot give confidently wrong regulatory guidance.
Financial AI fails quietly. And when it does, the liability is yours.
SichGate tests AI in financial workflows for the failure modes auditors and regulators care about — before they reach production.
Findings map to GDPR, OWASP LLM Top 10, and NIST AI RMF.
LEGAL
critical constraints
cannot hallucinate a contract clause.
cannot leak a privileged document reference.
cannot give a confidently wrong answer that ends up in a filing.
Legal AI operates where the cost of a single failure is high.
SichGate tests legal AI deployments for the failure modes that matter — and delivers findings formatted for legal and compliance review. No translation required.
Findings mapped and formatted for legal and compliance review.
[ 08 ] Services
Model Integrity
Assessment
[ MANAGED ]
Submit your model for a full adversarial evaluation. Findings, severity scores, and compliance mappings delivered within 24 hours. No setup required.
[ SELF-SERVE ]
Run evaluations independently and monitor model behavior over time. Built for engineering teams that need continuous, integrated testing.
[ 09 ]
ARTICLES
Writing on model integrity.
[ 01 ]June 2026
Why Base-Model Benchmarks Fail After Fine-Tuning
The benchmark describes a checkpoint that no longer exists.
READ →
[ 02 ]June 2026
Model Integrity Testing Is Not Red Teaming, Evals, or Guardrails
Three reasonable guesses. All three wrong in instructive ways.
READ →
[ 03 ]June 2026
The Release Gate Your Model Pipeline Is Missing
Code does not reach production without passing tests. Models do.
READ →
[ 09 ] EARLY ACCESS
We are working directly with teams deploying small language models.
If you want to:
- — Check whether a fine-tune changed model behavior.
- — Validate a quantized build before release.
- — Add adversarial checks to CI/CD.
- — Build evidence for internal risk review.
[ 10 ] FAQ
SichGate is an automated model integrity testing platform for small language models that checks whether a model's behavior changes after fine-tuning, quantization, or compression. It helps teams detect safety drift before deployment and generate audit-ready evidence for security, compliance, and enterprise review.
Many organizations deploy adapted models without independently validating how those changes affected safety behavior. SichGate closes that gap by testing model variants with repeatable adversarial evaluations and highlighting regressions that standard benchmarks may miss.
SichGate is designed for AI vendors and internal AI teams shipping custom or adapted models into regulated environments such as healthcare, fintech, legal, and enterprise settings. It is especially relevant for teams that need credible model-layer evidence for procurement, compliance, or customer assurance.
model types
input methods
SichGate can test base, fine-tuned, compressed, and quantized small language models. It supports models accessed via local weights, HuggingFace model ID, or API endpoint, depending on the testing tier and scope.
SichGate covers 32 attack categories across 8 tactic areas, including prompt injection, jailbreaks, sycophancy, context manipulation, data leakage, role subversion, membership inference, model extraction, and reasoning chain manipulation. It is built to expose safety regressions that may appear only after a model is adapted or compressed.
Quantization-aware testing compares a base model with fine-tuned or quantized variants to identify safety drift introduced by compression or adaptation. This helps teams see whether a model that looked safe before deployment behaves differently in production.
Yes, SichGate maps findings across eight frameworks, including the EU AI Act, GDPR, HIPAA, NIST AI RMF, ISO/IEC 42001, OWASP LLM Top 10, and MITRE ATLAS. It does not issue compliance certificates, but it does provide structured, audit-ready integrity evidence.