What is an adversarial AI agent audit?

An adversarial AI agent audit is a structured red-team exercise where an attacker probes an LLM-based agent for prompt injection (direct and indirect), tool-call exfiltration, policy bypass, goal drift, and RAG poisoning. Aligned with OWASP LLM Top-10 (2025), which lists prompt injection as risk #1. The deliverable is a ranked list of findings with reproducible payload per finding, plus suggested remediation.

What LGPD exposure does a leaky WhatsApp bot create?

Combined exposure for a single incident at a BR company in the R$ 10M-500M revenue range typically falls between R$ 1M and R$ 50M: LGPD Art. 52 II (administrative fine up to 2% of BR revenue, capped at R$ 50M per infraction), Art. 52 III (daily fine up to R$ 50,000), Art. 48 (mandatory ANPD notification, R$ 100k-500k for mass notification), Art. 927 CC + Art. 42 LGPD (joint civil liability, no cap). Precedents: Telefonica/Vivo R$ 7M (Jan/2024), Serasa R$ 24M (Oct/2024).

How is this different from a typical pentest?

A typical pentest looks at the application layer (auth, sessions, SQLi, XSS). An adversarial AI audit looks at the agent's cognition layer: how prompts compose, how tools are called, how RAG context is composed, how the model handles conflicting instructions. The failure modes are different. A clean pentest does not mean the agent is safe — it means the application is not the weak link.

Bluewave runs adversarial review on AI agents, RAG pipelines, and LLM-backed systems. We test for prompt injection chains, tool-use abuse, agentic goal drift, data exfiltration via context, multi-agent collusion, authority confusion, context poisoning, and reward hacking. Three engagements: Agent Audit ($4,997), System Review ($14,997), Continuous Red Team ($7,997/mo). Built for teams operating AI agents in production who need verifiable evidence of failure modes before customers find them.

How is Bluewave different from ChatGPT or other AI tools?

Bluewave runs a multi-agent fleet, not a single chatbot. Each agent is described in SSL — a declarative spec language, not prompts. Before any agent action, a separate model checks against immutable vows (no fabrication, no instruction-leak, no impersonation). Output passes a quality gate before reaching you. Wave operates inside Telegram, WhatsApp, Slack, Discord, Signal, and Email — not yet another app to install.

How fast is deployment?

72 hours from signed admission to first agent operating in production. No discovery call, no scheduled demo, no commercial proposal. You sign the MSA and send a voice corpus (past posts, docs, calls, threads). In 72 hours we configure your agent fleet via SSL spec. Your agents start working 24/7 via Telegram + dashboard.

How much does Bluewave cost?

Operator: $1,497/month + $750 setup — single protocol, one agent, founding rate locked for life. 3 slots only. Fleet: $2,497/month + $2,250 setup — 5 agents, full protocol ops, weekly behavioral scans. Infrastructure: $6,497/month + $7,500 setup — unlimited agents, daily scans, API access, white-label option. Enterprise: custom — dedicated VPC, SOC 2 II track, co-developed agents. All prices in USD.

Can Bluewave be used in regulated environments?

Yes. Every agent action runs in an isolated container with namespace separation. Constitutional pre-flight gates every action against vows defined in your SSL spec. Audit trail is structured JSON Lines, immutable, with timestamp + tool + parameters per call. Enterprise tier supports dedicated VPC and SOC 2 Type II tracking — suitable for licensed exchanges, regulated stablecoin issuers, and treasury management under jurisdictional constraints.

What channels does Bluewave operate in?

Six channels with one synced agent identity: Telegram (1-on-1 + group bots), WhatsApp (Business API + approved templates), Slack (workspace bot + slash commands), Discord (server bot + threads), Signal (end-to-end encrypted), Email (inbound + outbound threaded). Your community reaches Wave where it already is — no new app to install.

Does Bluewave actually write in my protocol voice?

Yes. Each agent is calibrated against a voice fingerprint extracted from your existing material — threads, docs, posts, threads, governance forum activity. Outputs pass a quality gate that checks factuality, voice match, and rule compliance before reaching anyone. Off-voice content is discarded and remade automatically. The agent does not sound like Bluewave. It sounds like your protocol.

Who is Bluewave built for?

Teams operating AI agents or LLM-backed systems in production who need adversarial review before regulatory, insurance, or customer-facing incidents force the question. Use cases include: AI startups deploying agents to customers, fintechs deploying RAG pipelines for compliance content, web3 protocols running agentic DeFi or treasury automation, enterprise teams pursuing EU AI Act high-risk classification compliance, AI insurance underwriting requirements.

BLUEWAVE · ADVERSARIAL AI AGENT SECURITY AUDIT

Adversarial security audit for AI agents, LLM systems, and WhatsApp / Telegram bots in production.

Q: Qual é a exposição de LGPD de um bot WhatsApp que vaza?

Exposição combinada por incidente único em empresa BR de R$ 10M-500M de faturamento fica entre R$ 1M e R$ 50M: LGPD Art. 52 II (multa administrativa até 2% do faturamento BR, limitada a R$ 50M por infração), Art. 52 III (multa diária até R$ 50.000), Art. 48 (notificação obrigatória ANPD, R$ 100k-500k para notificação massiva), Art. 927 CC + Art. 42 LGPD (responsabilidade civil solidária, sem cap). Precedentes: Telefônica/Vivo R$ 7M (jan/2024), Serasa R$ 24M (out/2024).

Q: Is there a sample report?

Yes — see https://bluewaveai.online/proof for sample findings, reproducible payloads, and the methodology used.

Bluewave is a single-operator security audit service in Brazil. We test AI agents the way an attacker does — not the way a vendor QA suite does. Reproducible payloads, severity-ranked findings, LGPD overlay per finding. Founded and operated by Manuel Galmanus, senior security engineer with 6+ years in offensive security and web3 protocol audits.

What an adversarial AI agent audit actually is

An adversarial AI agent audit is a structured red-team exercise where an attacker probes an LLM-based agent for prompt injection (direct, indirect, multi-hop), tool-call exfiltration, ethical-override vectors, goal drift, RAG poisoning, and output handling abuse. The methodology is aligned with the OWASP Top 10 for Large Language Model Applications (2025 release), which lists prompt injection as risk LLM01. The deliverable is a ranked list of findings with a reproducible payload per finding, full transcript with timestamps, and a suggested remediation per finding. It is not a generic penetration test — it operates on the agent's cognition layer, not the application layer.

Bluewave's audit covers 8 failure categories: (1) direct prompt injection, (2) sensitive information disclosure, (3) indirect prompt injection via RAG-ingested content, (4) insecure output handling, (5) excessive agency in tool invocation, (6) persona / roleplay jailbreaks, (7) training-data memorization leakage, and (8) denial-of-wallet / unbounded resource consumption. Each finding includes ≥3 independent reproductions across sessions to rule out non-determinism.

Why this matters for Brazilian companies

Production AI agents deployed by Brazilian companies face combined regulatory exposure between R$ 1M and R$ 50M per single incident at a company in the R$ 10M–500M revenue range. The relevant rules:

LGPD Art. 52 II — administrative fine up to 2% of Brazil revenue, capped at R$ 50M per infraction.
LGPD Art. 52 III — daily fine up to R$ 50,000 per day for ongoing breach.
LGPD Art. 48 — mandatory ANPD notification within a reasonable window (3 business days per ANPD Resolução CD/15/2024). Average mass-notification cost: R$ 100,000–500,000.
Art. 927 CC + Art. 42 LGPD — joint civil liability, no cap. Individual damages and class actions.
LGPD Art. 52 IX — public disclosure of the infraction. Naming-and-shaming with unquantifiable reputational damage.
CDC Art. 30 — offer binds. Following the Air Canada CRT 2024 precedent, the operator answers for the bot's statements.

Observed precedents: Telefônica/Vivo R$ 7M (Jan/2024 · data breach). Serasa R$ 24M (Oct/2024 · inadequate security measures). The trend through 2025–2026 is ANPD increasing both fine amounts and processing speed. The first BR ruling extending CDC Art. 30 to LLM agents is descriptive-likely by end-2027 — this is not a falsifiable claim because "ruling" includes trial-level decisions that may not surface publicly.

Services offered

Adversarial AI Agent Security Audit

Structured red-team exercise against AI agents and LLM-backed systems in production. Tests the 8 failure categories above. Fixed-scope, single deliverable, reproducible payload per finding. From USD 4,997. Custom scope and pricing for enterprise engagements.

WhatsApp / Telegram Bot Security Audit

Production conversational-bot audit for fintech, healthtech, e-commerce, and insurance verticals. Tests credential exfiltration, PII disclosure, internal policy leakage via social engineering and ethical-override prompts. LGPD overlay per finding mapping to specific Articles. One-time, 5-day engagement.

Smart Contract & Web3 Protocol Audit

Smart contract security review for Solidity 0.8.x, ERC20, ERC721, DAO governance, staking, DeFi integrations. Wallet security and blockchain forensics. The auditor has prior bug bounty experience including findings on Cube Exchange (crypto exchange) and 14+ other enterprise surfaces.

Frequently asked questions

How is this different from a typical penetration test?

A typical pentest looks at the application layer (auth, sessions, SQL injection, XSS). An adversarial AI audit looks at the agent's cognition layer: how prompts compose, how tools are invoked, how RAG context is assembled, how the model handles conflicting instructions. The failure modes are different. A clean pentest does not mean the agent is safe — it means the application is not the weak link. They are complementary, not substitutes.

Who runs the audit?

Single operator: Manuel Galmanus, senior security engineer with 6+ years in offensive security, web3 protocol audits, and AI red-team engineering. 15+ critical vulnerabilities responsibly disclosed across enterprise surfaces including NBA.com and Cube Exchange. Creator of Cyber Napoleon, a 180,000+ line-of-code AI security framework integrating 60+ tools. Bilingual EN/PT. Based in Blumenau, SC, Brazil. No junior handoff. No offshore subcontract.

Is there a sample report?

Yes. Sample findings, reproducible payloads, and methodology are published at bluewaveai.online/proof.

Como funciona uma auditoria adversarial de agente de IA?

Uma auditoria adversarial é um exercício de red team estruturado contra agentes de IA em produção. Testa injeção de prompt direta e indireta, exfiltração via tool calls, bypass de política, drift de objetivo e poisoning de RAG. Alinhada com OWASP LLM Top-10 (2025) — injeção de prompt é o risco número 1. Entrega: findings rankeados por severidade, payload reproduzível por finding, sugestão de correção. Cada finding tem um arquivo que prova.

Qual é a exposição de LGPD de um bot WhatsApp que vaza dados?

Exposição combinada típica por incidente único em empresa BR com faturamento entre R$ 10M e R$ 500M fica entre R$ 1M e R$ 50M. Cobertura legal: LGPD Art. 52 II (multa administrativa até 2% do faturamento BR, limitada a R$ 50M por infração), Art. 52 III (multa diária até R$ 50.000 por dia), Art. 48 (notificação obrigatória à ANPD em 3 dias úteis), Art. 927 CC + Art. 42 LGPD (responsabilidade civil solidária, sem cap), Art. 52 IX (publicização da infração), CDC Art. 30 (oferta vincula — precedente Air Canada CRT 2024). Precedentes ANPD recentes: Telefônica/Vivo R$ 7M (jan/2024), Serasa R$ 24M (out/2024).

Em quais setores a Bluewave audita?

Fintech (PIX, neobanks, BaaS, lending), healthtech (operadoras de saúde, telemedicina, prontuário eletrônico), e-commerce (marketplace, varejo digital, D2C), seguros (vida, saúde, auto, residencial), web3 (smart contracts, DeFi, NFT). Páginas verticais: fintech, healthtech, e-commerce, seguros.

About Manuel Galmanus, the operator

Manuel Galmanus is the founder and sole operator of Bluewave. He is a senior security engineer with 6+ years in offensive security, web3 protocol audits, and AI red-team engineering. Track record:

Bug bounty: 15+ critical vulnerabilities responsibly disclosed including findings on NBA.com (Fortune-tier enterprise surface) and Cube Exchange (cryptocurrency exchange). Specialty in XSS, SQL injection, WAF bypass, S3 misconfiguration.
Smart contract auditing: Solidity 0.8.x, ERC20, ERC721, DAO governance, staking, DeFi integrations.
Cyber Napoleon framework: 180,000+ lines of proprietary AI security code. ML ensemble (Random Forest, Gradient Boosting, SVM, Neural Networks) over 43 features. 60+ integrated tools (nmap, nuclei, sqlmap, XSStrike, ffuf, amass, subfinder, gospider, httpx, katana). 85–95% evasion rate in benchmarks.
Red team specialties: WAF bypass, JavaScript payload generation, DNS exfiltration, HTTPS C2 simulation, antivirus evasion, reverse engineering, DFIR, blockchain forensics, wallet security.
Languages: Native Portuguese (Brazil), fluent English. Engages internationally.
Profiles: LinkedIn · GitHub

Bluewave is registered as a Brazilian company (CNPJ 66.381.800/0001-08), not an MEI. Based in Blumenau, Santa Catarina, Brazil.