Results for “data-race-safety”

Audits Rust codebases for safe/unsafe boundary issues, memory safety in unsafe blocks, concurrency hazards, panic-induced DoS, FFI safety, and async runtime mistakes.

Implements AI safety guardrails including input validation, output filtering, robustness testing, human oversight, and monitoring to prevent harmful outputs and ensure system reliability.

AI safety and responsible AI practices

Enforce idiomatic Rust patterns for ownership, error handling, enums, traits, concurrency, and best practices to build safe, performant applications.

Detect and exploit race condition vulnerabilities in web applications using Turbo Intruder's single-packet attack technique to bypass rate limits, duplicate transactions, and exploit time-of-check-to-time-of-use flaws.

Review protected asset policy and risky local data surfaces.

Applies the FedRAMP framework to identify, assess, and mitigate security risks in systems, processes, and data handling.

Idiomatic Rust patterns, ownership, error handling, traits, concurrency, and best practices for building safe, performant applications.

Race condition and TOCTOU testing for web apps. Use when testing one-time operations, concurrent HTTP abuse, rate-limit bypass, Turbo Intruder gates, HTTP/2 single-packet attacks, and CWE-362-style synchronization gaps.

Laravel 安全最佳实践，涵盖认证/授权、验证、CSRF、批量赋值、文件上传、密钥、速率限制和安全部署。

Hardens Laravel applications against common vulnerabilities with guidance on authentication, authorization, validation, CSRF, mass assignment, file uploads, secrets, rate limiting, and secure deployment.

Idiomatic Rust patterns, ownership, error handling, traits, concurrency, and best practices for building safe, performant applications.

Guides writing idiomatic Rust with ownership, error handling, traits, concurrency, and best practices for safe, performant code.

Idiomatic Rust patterns, ownership, error handling, traits, concurrency, and best practices for building safe, performant applications.

地道的Rust模式、所有权、错误处理、特质、并发，以及构建安全、高性能应用程序的最佳实践。

Execute and analyze AI Data Poisoning attacks. By subtly injecting malicious or targeted misinformation into an LLM's training or fine-tuning dataset, an attacker can covertly manipulate the model's future outputs, implant backdoors, or enforce biases without altering the model architecture.

Laravel security best practices for authn/authz, validation, CSRF, mass assignment, file uploads, secrets, rate limiting, and secure deployment.

Detect poisoned training data and backdoored models across the ML pipeline using statistical analysis, activation clustering, and spectral signatures.

Use when a Supabase app needs Row Level Security policies for multi-tenant access, ownership checks, roles, service paths, or safe data exposure. Trigger on requests to design or review Supabase RLS and SQL authorization logic.

Use when tenant data protection, data privacy compliance, data encryption, or data governance is needed. This agent specializes in SaaS data protection within the SaaSForge AI ecosystem.

Conducts security testing of REST, GraphQL, and gRPC APIs to identify vulnerabilities in authentication, authorization, rate limiting, input validation, and business logic using the OWASP API Security Top 10 framework.

Provides safe, reversible database schema change patterns for PostgreSQL, MySQL, and common ORMs, with guidance on zero-downtime deployments, rollbacks, and migration tooling.

Provides a structured risk scoring methodology for Data Protection Impact Assessments aligned with ENISA threat taxonomy and ISO 29134. Covers likelihood and severity assessment, risk matrix construction, inherent vs residual risk calculation, and risk appetite thresholds per EDPB WP248rev.01 guidance. Keywords: risk scoring, DPIA risk matrix, likelihood, severity, ENISA, ISO 29134, residual risk, risk appetite.

Deploys and configures Suricata IDS/IPS with Emerging Threats rulesets, EVE JSON logging, and custom rules for real-time network traffic inspection, threat detection, and integration with SIEM platforms.

OWASP Top 10 + Supabase-first hardening burndown. Use when "security audit plan", "OWASP audit", "hardening plan", or "security burndown". App-layer auth flows → audit-auth-flows. Table RLS → plan-rls-audit. Key rotation → plan-secrets-audit. App LLM attacks → audit-llm-security.

CI security scanning: secrets, deps, SAST, triage, expiring exceptions

Security Practices — Cryptographic Safety, Memory Zeroization & Hardened I/O

Protect API Gateway endpoints with AWS WAF by configuring managed rule groups, rate limiting, bot control, IP reputation filtering, and monitoring.

Django 安全最佳实践、认证、授权、CSRF 防护、SQL 注入预防、XSS 预防和安全部署配置。

`analysis-agent`/`task-agent`/`review-agent`: primary-Skill-selected for races, locks, optimistic conflicts, or worker overlap; never task owner; skip without concurrency impact.

Audit a Supabase/Postgres project for Row-Level Security and access-control gaps, then produce a phased remediation plan. Use when "RLS", "is my Supabase secure", "anyone can read my data", "lock down my tables". App-layer session/route gates → audit-auth-flows.

Hardens Django applications against common vulnerabilities with production settings, authentication, authorization, SQL injection and XSS prevention, and secure deployment configurations.

Django security best practices, authentication, authorization, CSRF protection, SQL injection prevention, XSS prevention, and secure deployment configurations.

Higher-precedence research helper with risky setup.

Persists app data on Android using Room, SQLite, SharedPreferences, DataStore, and encryption.