Results for “attack-surface-reduction”

Builds an external attack surface management (EASM) program using Shodan, Censys, and ProjectDiscovery tools for asset discovery, subdomain enumeration, service fingerprinting, and exposure scoring.

Reduces a webpage to a structural skeleton by tokenizing content in the browser and applying LLM reasoning to collapse repeated patterns.

Protect APIs from abuse and resource exhaustion by implementing rate limiting with token bucket, sliding window, and fixed window algorithms using Redis-backed counters, API gateway plugins, or application middleware.

Configures memory protection mechanisms including DEP, ASLR, CFG, and Windows Exploit Protection to harden endpoints against buffer overflows, ROP chains, and code injection.

Executes containment strategies to stop active adversary operations and prevent lateral movement during a confirmed security breach, using network segmentation, endpoint isolation, credential revocation, and access control modifications.

Rate Limiting — Agent Arena

Analyzes and optimizes firewall rules to reduce attack surface while maintaining connectivity

Agent validation v4.3.0 — Make agents act effectively by disabling harmful actions, lowering gates, and injecting cross-run learning

Strategic patterns for solving intractable problems through cascading approximation, self-improvement, and heterogeneous evaluation from DeepMind's AlphaGo system

STRIDE Threat Model

Guides authorized Active Directory security research covering Kerberos attacks, AD CS vulnerabilities, BloodHound path analysis, NTLM relay, and domain privilege escalation techniques.

Proactively hunts for Advanced Persistent Threat activity using hypothesis-driven searches across endpoint telemetry, network logs, and memory artifacts.

Simulates bandwidth throttling and network degradation attacks using tc, iperf3, and Scapy in authorized environments to test quality-of-service controls, application resilience, and network monitoring detection of traffic manipulation attacks.

Map adversary TTPs against organizational assets using the MITRE ATT&CK framework, assess detection coverage gaps, and prioritize defensive investments.

Add distributed training support to any PyTorch script with minimal code changes using a unified API for DDP, DeepSpeed, FSDP, and mixed precision.

Implement API abuse detection using token bucket, sliding window, and adaptive rate limiting algorithms to prevent DDoS, brute force, and credential stuffing attacks.

Harden container images by minimizing attack surface, removing unnecessary packages, implementing multi-stage builds, configuring non-root users, and applying CIS Docker Benchmark recommendations.

`analysis-agent`/`task-agent`/`review-agent`: use for changed assets, trust boundaries, reachable abuse paths, impact, or control placement; skip without a security delta.

AIDefence security layer with prompt injection blocking, input validation, sandboxed execution, output sanitization, and STRIDE threat modeling.

Identifies error-prone APIs, dangerous configurations, and footgun designs that enable security mistakes in API designs, configuration schemas, and cryptographic library ergonomics.

Analyzes .NET project code coverage and CRAP (Change Risk Anti-Patterns) scores to identify risk hotspots, methods blocking coverage gains, and prioritize where to add tests.

Compares Trailmark code graphs at two source code snapshots to surface security-relevant structural changes that text diffs miss, such as new attack paths, blast radius growth, and privilege boundary modifications.

Hardens Windows endpoints using CIS Benchmark recommendations to reduce attack surface, enforce security baselines, and meet compliance requirements.

Reduce container attack surface by building application images on Google distroless base images that contain only the application runtime with no shell, package manager, or unnecessary OS utilities.

Agent validation overhaul: reward weight overrides, fitness decline gate, pinned data, staged experiments

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.

Maps observed adversary behaviors, security alerts, and detection rules to MITRE ATT&CK techniques and sub-techniques to quantify detection coverage and guide control prioritization.

Optimize RF PCB dimensions for bandwidth, return loss, or area via patternsearch and surrogateopt with constraints. TRIGGER: user asks to optimize an RF PCB component for performance (bandwidth, return loss, insertion loss, area) or apply constraints to a design. Invoke BEFORE writing optimization code — RF PCB Toolbox has a built-in optimize() function that differs from generic fmincon/ga approaches. SKIP: designing a component from scratch without an optimization objective (use the specific matlab-design-pcb-* skill), EM analysis without optimization (use matlab-analyze-em), material/stackup setup only (use matlab-manage-pcb-material).

Map MITRE ATT&CK coverage to identify detection gaps, prioritize rule development, and measure SOC detection maturity against adversary techniques.

Audits over-engineered codebases by factoring layers into live obligations, quotienting redundant distinctions, ablating unearned surface, and normalizing survivors while preserving required behavior. Produces an evidence-backed Reduction Certificate, cuts, migration phases, proof signals, and rollback.

`analysis-agent`/`task-agent`/`review-agent`: use for render sinks, browser state, server fetch, upload, redirect, cross-origin, or embedding changes; skip without web exposure.

Detect and test for OWASP API3:2023 Broken Object Property Level Authorization vulnerabilities including excessive data exposure and mass assignment attacks.

Expert guidance for architecting, developing, and operating SurrealDB 3, covering SurrealQL, multi-model data modeling, vector search, security, deployment, performance tuning, SDK integration, and ecosystem tools.

Red team tactics principles based on MITRE ATT&CK. Attack phases, detection evasion, reporting.

Run PyTorch training across GPUs with minimal changes.