// security research
· from $20/moAn uncensored AI
for security research.
Original security research is the slow, expensive work that produces public CVEs, conference talks, and the threat intel everyone else copy-pastes.
A senior researcher producing two real CVEs a year is exceptional output; most spend the year on plumbing and never publish. The major coding assistants will help with parts of the work — until your prompt mentions vulnerability, exploit, attack, or anything else that triggers their content policy. Then you spend half your research time arguing with the tool. TartarusAI handles the parts that drain your week — firmware unpacking, attack-surface enumeration, PoC validation, disclosure drafting — so your time goes to the actual research. Same enterprise-grade runtime safety guards. No content-policy refusal on legitimate vuln research. The agent treats security research the way every recognised research lab already does: as a professional discipline with public goods as the output. Hypothesis-driven exploration, harness writing, primitive identification, exploitation chain construction, reliability hardening, disclosure drafting — the agent does the boilerplate so you spend the year producing CVEs instead of fighting your tooling.
- ✓Zero refusals on offsec work
- ✓Custom-tuned model — not a wrapper
- ✓Runtime guards on by default
- ✓256K context, sub-2s TTFT
refund if it ever refuses · no card on file · crypto-only · cancel any time
❯ research the attack surface of this firmware blob — find auth bugs
target: smart-home device firmware, signed research
▎▣ unpacked firmware (squashfs root)
▎▣ identified 3 candidate auth-bypass paths
▎+ wrote analysis.md + 2 PoCs
▎+ suggested CVE-disclosure draft
done.// what it does
What you ship
when nothing refuses.
Firmware + embedded research
Unpack squashfs / cramfs / jffs2 / proprietary formats, identify exposed services, enumerate the binary attack surface, port pseudocode to running PoCs. Particularly useful for IoT / OT / automotive / industrial-control targets where the firmware is the primary attack surface and the unpacking + analysis is most of the engagement.
Novel-vuln discovery + validation
Hypothesis-driven exploration with the agent writing harnesses, fuzzers, and sanity checks as you iterate. The verification gate keeps you honest — bug is not a bug until the PoC reproduces. Particularly strong on protocol fuzzing, format fuzzing, and the long-tail bug classes that require custom harness construction (race conditions, kernel-level concurrency, distributed-system invariants).
CVE-disclosure drafting
Once the bug is real, the agent ghostwrites the disclosure — title, severity, CVSS scoring, repro steps, impact statement, suggested remediation. MITRE / vendor-specific format on request. Includes vendor disclosure email drafts (formal / friendly / firm tone), follow-up scheduling, timeline-tracking templates for the disclosure window.
Threat-intel + research writeups
Research findings → blog-quality post. TTP mapping, IOC tables, executive summary, technical deep-dive, supporting code listings, screenshot annotation suggestions. Style detection per audience (security-blog technical, conference-talk narrative, academic-paper formal).
Reproducibility + open-source PoC release
For research that goes public after disclosure, the agent helps prepare the open-source PoC repo — clean code, tests against multiple target versions, README, build instructions, attribution and licensing boilerplate. Cuts the friction of releasing PoCs that the broader community can actually use.
Cross-disciplinary research support
Cryptographic protocol analysis, distributed-system invariant checking, supply-chain attack research (npm / PyPI / cargo / Go modules), AI/ML security research (model extraction, membership inference, prompt injection at scale), web3 / smart-contract security. The agent reads the relevant background literature and helps you write the harness for whatever subdiscipline you are exploring.
// workflow
A typical research arc
You start with a hypothesis: "this device class has an attack surface that nobody has examined publicly," "this protocol family has a flaw in its authentication handshake," "this dependency chain could be supply-chain-attacked." You drop the relevant context into the agent — vendor advisory, public source code, leaked firmware, paper that makes a related claim — and ask for the read.
The agent proposes the research direction. You sanity-check. The agent writes the first harness. The verification gate runs it; if the harness instruments correctly, you keep going. The loop is fast: you steer, the agent writes, you sanity-check, the agent iterates. For research that takes a senior researcher a month of calendar time, the agent compresses the boilerplate to days, leaving the senior time for the parts that actually require human judgement (which hypothesis to pursue, when to pivot, when to publish).
When the bug is real, the agent helps with the disclosure prep. Vendor disclosure email in the right tone, MITRE CVE submission draft, timeline-tracking template for the disclosure window, public writeup that goes live when the embargo lifts. Cuts the worst part of original research — turning the work into something the rest of the community can act on.
// discipline
Why original research needs uncensored tooling
Original security research is, by definition, work on attacks that have not been disclosed yet. The major coding assistants treat any prompt that looks like attack research with maximum suspicion — their content policies cannot distinguish between a research lab disclosing a new vuln and someone trying to weaponise that vuln before the disclosure lands. The result: senior researchers either rephrase through jailbreak templates that get patched next month, or accept neutered output that misses the point.
TartarusAI removes the content layer. Hypothesis-driven exploration, novel-vuln discovery, exploitation primitive construction, reliability hardening — all in scope. The agent reasons over the relevant subsystem (firmware, protocol, codebase, model architecture) without lecturing about why the work is being done. Runtime safety guards stay in place; they protect your project from the agent breaking things, not the model from your prompt.
For research labs producing CVEs, conference talks, and academic papers, this is the structural difference between AI tooling that accelerates the work and AI tooling that fights it. The economics favour the dedicated tool the moment you measure your senior-researcher time honestly.
// disclosure
Coordinated disclosure done right
Coordinated disclosure is partly a technical problem and mostly a communication problem. Vendors have legal, PR, and engineering perspectives that need to be managed. Disclosure timelines need to be defended without becoming adversarial. Public writeups need to be defensible under scrutiny.
TartarusAI helps with the parts that compose into a clean disclosure: the technical write-up that the vendor engineering team can act on, the executive summary that the vendor PR team can communicate around, the timeline-tracking template that keeps the disclosure window from drifting into "we forgot about this," the follow-up email drafts that escalate appropriately if the vendor goes silent.
For researchers who publish at the major conferences (Black Hat, DEF CON, USENIX, IEEE S&P, CCS, NDSS), the agent helps with the conference-paper structure — abstract, motivation, related work, technical contribution, evaluation, related-but-distinct work, ethics statement, responsible disclosure section. Cuts the calendar time of paper writing without sacrificing the technical depth that gets papers accepted.
// questions
What people actually ask.
Does it work for novel research / 0-day discovery?+
Will it help draft responsible-disclosure communication?+
Can I keep my research private during the disclosure window?+
Does it cover hardware / firmware / embedded research?+
What about academic / conference paper writing?+
Does it handle cryptographic protocol analysis?+
Can it help with supply-chain attack research?+
What about AI / ML security research?+
// ready
Stop fighting refusals.
Start shipping the engagement.
One tier covers most engagements at $20/month. If the agent ever refuses, hedges, or returns neutered output on legitimate engagement work, we refund — see the refund policy.
refund if it ever refuses · no card on file · crypto-only