$ adversarial services --list
Services
Everything we do runs on the same loop: attack like an adversary, deliver like an engineer. Each service is available standalone or as part of a continuous engagement.
01CI/CD red-teaming
Continuous adversarial review embedded in your pipeline. Agents model each change the way an attacker would — probing for injection, broken auth, logic flaws, race conditions, and misconfigured pipeline permissions.
- Exploit attempts, not just static pattern matching
- Reachability analysis to kill false positives
- Severity-ranked findings with reproduction steps
02Automated bug-fix agents
Findings without fixes are just homework. Our patcher agents write minimal, tested fixes and push them as branches or PR comments your team can review line by line.
- Patches validated against your test suite
- Minimal diffs — no drive-by refactoring
- Regression tests included with every fix
03Security issue triage & patching
Drowning in scanner output? We ingest findings from your existing tools, deduplicate them, confirm exploitability, and turn the real ones into merge-ready patches.
- Ingests SARIF and scanner output from existing tooling
- Confirms exploitability before it touches your queue
- SLA-backed triage for critical and high findings
04Adversarial prompt testing of in-repo AI agents
Your repo now contains AI agents — and they have an attack surface. We probe your agent configs, system prompts, and tool permissions for prompt injection, including poisoned CLAUDE.md-style instruction files, malicious README content, and hostile code comments designed to hijack coding assistants.
- Detects injected instructions in agent config and doc files
- Tests tool-use and permission boundaries of coding agents
- Recommends hardened configs and guardrails
05Continuous pipeline monitoring
Point-in-time pentests go stale the next commit. Our agents watch every merge, dependency bump, and config change, alerting only when something is genuinely exploitable.
- Dependency and supply-chain watch on every lockfile change
- Drift detection for secrets, permissions, and IaC
- Weekly digest: what changed, what matters, what's patched
06Create a model with us
Need a model that knows your codebase, your stack, your bug taxonomy? Our professional services team builds custom finding-and-fixing models with you — on your data, or on synthetic data we generate for you.
- BYOD — bring your own data: we curate, de-identify, and train on your repos, PR history, and incident reports
- No usable data? We generate validated synthetic bug/vulnerability datasets in your languages and frameworks
- You own the resulting model weights and evaluation harness
07Not sure where to start?
Start with a security review of one repository. We'll run the full agent stack against your code and walk you through everything it finds — confirmed, reproduced, and with patches proposed.
Book a security review →$ adversarial models --create
Create a model with us
Our general-purpose agents are strong generalists. A custom model is a specialist: trained on the way your team writes, breaks, and fixes code. Engagements run 6–12 weeks with our professional services team embedded alongside yours.
Path A — bring your own data
You already have the raw material: repositories, code-review threads, PR histories, incident postmortems, ticket queues. We handle the pipeline.
- Data audit: we assess coverage, quality, and licensing before any training
- De-identification and secret scrubbing, verified with our own scanners
- Task curation: bug introduction/fix pairs, vuln/patch pairs, review-comment actions
- Training runs inside your cloud boundary or ours — your call
Path B — synthetic data generation
No usable corpus, or data you can't share? We manufacture one. Our generator agents create realistic, validated training data targeted at your stack.
- Seeded from our validated attack library and your architecture profile
- Every synthetic sample compiled, executed, and verified — no hallucinated bugs
- Difficulty-graded curricula: from lint-level slips to multi-file race conditions
- Held-out benchmark included, so progress is measurable, not vibes
01Scope
One week. We profile your stack and failure modes, then propose a data plan (BYOD, synthetic, or hybrid) with a fixed quote.
02Build & evaluate
Iterative training loops with weekly eval reports against a benchmark we agree on up front. You can stop at any checkpoint and keep the artifacts.
03Hand over
You get the weights, the eval harness, the data lineage report, and optional deployment into your pipeline as a private agent.