Results for “anti-spoofing”

Detect and prevent ARP spoofing attacks using ARPWatch, Dynamic ARP Inspection, Wireshark analysis, and custom Python monitoring scripts to protect against man-in-the-middle interception.

Simulates ARP spoofing attacks in authorized lab or pentest environments using arpspoof, Ettercap, and Scapy to demonstrate man-in-the-middle risks, test network detection capabilities, and validate ARP inspection countermeasures.

Configure email security gateways like Microsoft Defender, Proofpoint, and Mimecast to detect and block targeted spearphishing attacks using impersonation protection, URL detonation, and attachment sandboxing.

Identifies and exploits IPv6-specific vulnerabilities including SLAAC spoofing, Router Advertisement flooding, and IPv6 tunneling during authorized assessments to test dual-stack security controls and IPv6-aware network defenses.

Configure Google Workspace advanced phishing and malware protection settings including pre-delivery scanning, attachment protection, spoofing detection, and Enhanced Safe Browsing.

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.

Detect and respond to Adversary-in-the-Middle (AiTM) phishing attacks that use reverse proxy kits like EvilProxy, Evilginx, and Tycoon 2FA to bypass MFA and steal session tokens.

Audit a codebase, UI, or copy for machine-generated tells across prose, visual/UI, code, and structure/IA, then produce a phased de-slop burndown. Use when the user says "feels AI-generated", "looks like AI slop", "reads like ChatGPT", "feels generic/soulless", or wants an authenticity/voice pass before launch.

Detect brand impersonation attacks across domains, social media, mobile apps, and dark web channels to identify phishing campaigns, fake sites, and unauthorized brand usage.

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.

Detects fake reviews, Sybil rings, rating manipulation, and reputation laundering in ERC-8004 agent ratings across 20+ blockchains using the TrustLayer API.

Crafts and injects custom network packets using Scapy, hping3, and Nemesis during authorized security assessments to test firewall rules, IDS detection, protocol handling, and network stack resilience against malformed and spoofed traffic.

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

Simulates VLAN hopping attacks using switch spoofing and double tagging techniques in authorized environments to test VLAN segmentation effectiveness and validate switch port security configurations against Layer 2 bypass attacks.

Detects anomalous authentication patterns using UEBA analytics, statistical baselines, and machine learning to identify impossible travel, credential stuffing, brute force, password spraying, and compromised account behaviors across authentication logs.

Prevent domain spoofing and phishing by implementing SPF, DKIM, and DMARC email authentication protocols with DNS configuration and validation.

Detect AI-generated audio, images, video, and text, trace synthesis sources, apply watermarks, verify speaker identity, and analyze media intelligence using the Resemble AI platform.

Detect NTFS timestamp manipulation (MITRE T1070.006) by comparing $STANDARD_INFORMATION vs $FILE_NAME timestamps in the MFT using analyzeMFT and Python.

Detect sophisticated cyber-physical attacks that modify PLC logic while spoofing sensor readings, covering PLC integrity monitoring, process anomaly detection, and multi-stage attack chain detection.

Deploys deception technology including honeypots, honeytokens, and decoy systems to detect attackers who have bypassed perimeter defenses, providing high-fidelity alerts with near-zero false positive rates.

Detects credential stuffing attacks by analyzing authentication logs for login velocity anomalies, ASN diversity, password spray patterns, and geographic distribution of failed logins using statistical analysis on Splunk or raw log data.

Deploy honeytokens and canarytokens as decoy artifacts to detect intrusions with near-zero false positives.

Detect and defend against prompt injection hidden in documents, web pages, and images consumed by an agent.

Detect typosquatting, homograph phishing, and brand impersonation domains using dnstwist to generate domain permutations and identify registered lookalike domains targeting your organization.

Simulates SSL stripping attacks using sslstrip, Bettercap, and mitmproxy in authorized environments to test HSTS enforcement, certificate validation, and HTTPS upgrade mechanisms.

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