Red Teaming Engineer — LLM Attack Campaigns, Nero & Evidence | TestSavant.AI

SOLUTIONS — BY ROLE: RED TEAMING ENGINEER

Break Systems Systematically.
Harden What Matters. Prove It.

Design and run attack campaigns with a hybrid engine plus Nero. Findings flow into Auto-tune (Airflow) for guardrail retraining and redeploy through the TestSavant API on Ray. Every test, hit, and diff is recorded by the Data Synthesizer & Aggregator for evidence.

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Broader Attack Coverage

Nero variants + human packs; updated from research & incidents.

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Shorter Fix Windows

Auto-tune retrains guardrails, validates, and redeploys quickly.

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Evidence by Default

Artifacts for audits: attack logs, policy hits, diffs, lineage.

Red Teaming Workbench

Campaigns, packs, and evidence—wired to retraining and runtime.

Attack Packs & Campaigns

  • Injection, jailbreak, indirect injection via files/links
  • Exfiltration (PII/PHI), tool/action abuse, retrieval poisoning, weak citations
  • Scheduling, regression, and targeted sweeps before releases

Nero & Attack Knowledge DB

  • Nero learns from traces; generates novel, effective attacks
  • Attack Knowledge DB stores patterns/signatures + examples
  • New research/incidents update the memory for zero/few-shot creation

Auto-tune Retraining (Airflow)

  • Findings → dataset → retrain guardrails → validate → redeploy
  • Human approval optional; diffs & metrics archived
  • Challenger runs before promotion

Runtime Guardrails (API on Ray)

  • Policy enforcement on prompts, RAG, tool calls
  • Per-request telemetry; policy hits recorded
  • Black-box friendly for third-party providers

From Attack to Enforced Control

Close the loop with telemetry, diffs, and repeatable re-tests.

Attack / Failure Mode Runtime Guardrail Test Method Result
Prompt injection / jailbreak Block/transform; quarantine suspicious artifacts Nero variants + human lure suites Takeover attempts blocked; evidence logs
Indirect injection via files/links Sanitize/deny; isolate content; escalate Hidden instruction tests in PDFs/images Chain takeovers prevented
Exfiltration (PII/PHI) Detect → mask/tokenize before external calls Adversarial PII payloads; log path probes Lower leakage; masking proofs
Tool/action abuse Deny/transform risky function calls Function-call abuse suites Unsafe actions blocked
Retrieval poisoning / weak citations Require strong provenance; deny low-trust sources Source-integrity & citation checks Trustworthy answers; lineage
Drift / robustness regression Trigger Auto-tune; redeploy hardened guards Scheduled regression packs Controlled updates; tracked diffs

Architecture & Controls

Campaigns → findings → datasets → Auto-tune → runtime guardrails → telemetry back.

Guardrail Models (API on Ray)

  • Attach to prompts/RAG/tools
  • Per-request telemetry & policy hits

Red Teaming (Hybrid)

  • Automated + manual packs
  • Black-box friendly; scheduled runs

Auto-tune (Airflow)

  • Retrain → validate → redeploy guardrails
  • Diffs/metrics archived; rollback plans

Nero (Autonomous Attacker)

  • Learns from traces & knowledge base
  • Generates novel attacks for packs

Attack Knowledge DB

  • Patterns/signatures + examples
  • Retrieval memory for Nero

Data Synthesizer & Aggregator

  • Fuse telemetry, Nero, Red Teaming, synthetics
  • Clean datasets for training & re-tests

Evidence Support for AI Frameworks

Artifacts aligned to ISO/IEC 42001, ISO/IEC 23894, NIST AI RMF, GDPR Art 22 / 15(1)(h).

NIST AI RMF 1.0

  • Risk registers from attack results and monitoring evidence.

ISO/IEC 42001 (AIMS)

  • PDCA artifacts: diffs, validations, incident learnings.

ISO/IEC 23894:2023

  • Evidence across identification → analysis → treatment → re-test.

GDPR (Automated Decisions & Rights)

  • Explainability excerpts; human-review trails where required.

Frequently Asked Questions

How do I add a new attack pattern?

Add examples to the Attack Knowledge DB; Nero uses them for zero/few-shot generation and expands the pack automatically.

Will this work against closed models?

Yes. Black-box probing is supported. Runtime guardrails attach at the orchestration edge; no provider internals required.

Run a Campaign. See the Evidence.

Start with a high-impact pack. Watch Auto-tune harden guardrails and serve them live.

TestSavant.ai provides technology and evidence to support AI security testing and governance programs. Nothing on this page constitutes legal advice.

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