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Identify ransomware network indicators including C2 beaconing patterns, TOR exit node connections, data exfiltration flows, and encryption key exchange via Zeek conn.log and NetFlow analysis

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Analyzes network traffic captures and flow data to identify adversary activity during security incidents, including command-and-control communications, lateral movement, data exfiltration, and exploitation attempts. Uses Wireshark, Zeek, and NetFlow analysis techniques. Activates for requests involving network traffic analysis, packet capture investigation, PCAP analysis, network forensics, C2 traffic detection, or exfiltration detection.

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Identify ransomware network indicators including C2 beaconing patterns, TOR exit node connections, data exfiltration flows, and encryption key exchange via Zeek conn.log and NetFlow analysis

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Investigate USB device connection history from Windows registry, event logs, and setupapi logs to track removable media usage and potential data exfiltration.

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Analyzes network traffic captures and flow data to identify adversary activity during security incidents, including command-and-control communications, lateral movement, data exfiltration, and exploitation attempts. Uses Wireshark, Zeek, and NetFlow analysis techniques. Activates for requests involving network traffic analysis, packet capture investigation, PCAP analysis, network forensics, C2 traffic detection, or exfiltration detection.

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Design layered protections for AI agents against malicious instructions, tool misuse, and data leakage. Provides guidance and code examples for input checks, isolated tool execution, permission limits, and audit logging to help review and strengthen agent security.

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• Review defenses against malicious instructions
• Isolate tool execution and restrict permissions
• Assess data leakage risks between tenants

Plan structured adversarial tests for AI applications to assess safety bypasses, data leaks, and unauthorized tool use. Organize test prompts, record findings, and prioritize fixes using reporting guidance and an adaptable testing template.

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• Assess an AI feature before release
• Compare security behavior of candidate models
• Retest after a prompt injection or jailbreak incident

Plan security controls for LLM applications and AI agents, covering malicious instructions, data leakage, model tampering, and API access. Includes guidance and code examples for filtering outputs, enforcing permissions, isolating networks, and recording audit events.

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• Reviewing defenses against prompt injection in AI agents
• Filtering personal information from LLM responses
• Designing authentication and rate limits for LLM APIs

Trace mailbox abuse in a CTF sandbox, a controlled security challenge environment. The skill connects OAuth permissions, forwarding rules, delegated access, and message logs to concrete mail effects, documenting the smallest reproducible sequence and supporting evidence.

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• Trace how OAuth consent enables mailbox access
• Investigate forwarding and inbox rule effects
• Verify shared mailbox and delegated access

Investigate prompt injection in sandboxed security challenges by tracing how untrusted text changes an agent’s decisions or tool calls. Build a minimal reproduction and preserve evidence showing where external content crosses into trusted instructions or actions.

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• Trace instructions hidden in retrieved documents
• Investigate agent behavior changed by stored memory
• Reproduce altered tool arguments or secret exposure

Investigate suspicious mobile apps by examining permissions, network traffic, and runtime behavior. Use observed evidence to identify signs of data theft, SMS interception, credential stealing, or communication with attacker control servers, then classify the suspected malware.

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• Triaging suspicious APK or IPA samples
• Investigating possible data leaks from mobile apps
• Checking for SMS interception and credential theft

Build or tune a monitoring pipeline that learns normal user behavior from activity logs. Use Elasticsearch or OpenSearch to score unusual activity, compare colleagues in similar roles, and combine warning signs into insider threat alerts.

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• Building continuous monitoring for insider threats
• Scoring unusual logins and large downloads
• Comparing behavior across similar job roles

Analyze NetFlow v9 and IPFIX records, which summarize network connections, to spot traffic spikes, scanning, unusual outbound transfers, and periodic connections. Compare traffic against normal patterns and produce a prioritized findings report.

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• Investigating sudden increases in network traffic
• Checking unusual outbound transfers for possible data theft
• Finding periodic connections to external control servers

Analyze malware network captures to investigate command-and-control traffic, data theft, and downloaded files. Use packet analysis tools to identify suspicious destinations and recurring connections, extract artifacts, and create detection rules from observed patterns.

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• Analyzing malware sandbox network captures
• Investigating command-and-control traffic
• Tracing data theft and file downloads

Check AI agent skills before installation by inspecting code, instructions, dependencies, and file access without running the code. Receive a PASS, WARN, or FAIL verdict with findings ranked by severity and suggested fixes.

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• Checking an unfamiliar skill before installation
• Finding suspicious instructions in SKILL.md
• Reviewing scripts for credential theft or data leaks

Investigate how credentials can be exposed in CI/CD, the systems that build and deploy software. Covers environment variables, logs, credential stores, cloud trust settings, and runner tokens, with methods to assess access scope and defensive controls.

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• Review secret exposure in build environments
• Search build logs and caches for leaked credentials
• Assess trust settings between pipelines and cloud services

Assess API business workflows for flaws that automated scanners can miss. The skill examines skipped steps, simultaneous transactions, authentication tokens, GraphQL, and webhooks, helping testers document weaknesses and their financial or operational impact during authorized assessments.

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• Reviewing order and payment workflow integrity
• Testing concurrent coupon redemption or transfers
• Assessing JWT validation and GraphQL controls

Assess AWS, Azure, and GCP environments by identifying identities, permissions, and paths to broader access or data exposure. Covers cloud storage, secrets, cross-account trust, and CI/CD, the automated systems used to build and deploy software.

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• Planning an AWS, Azure, or GCP security assessment
• Tracing privilege escalation from current permissions
• Reviewing CI/CD credentials and OIDC trust settings

Test LLM applications for malicious instructions, information leaks, and unsafe tool use. The skill runs attack cases and checks observable evidence, helping distinguish suspicious replies from reproducible failures with concrete impact.

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• Test chat APIs for prompt injection
• Check document-based AI for hidden instructions
• Verify unsafe tool calls by an AI agent

Review CI/CD pipelines, the workflows that build and release software, for unsafe code execution, exposed secrets, and excessive permissions. Combine automated scanning with manual checks to document reproducible findings and suggested fixes.

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• Reviewing GitHub Actions workflows
• Checking secrets and token permissions
• Inspecting self-hosted runners

Investigate LDAP and XPath injection in directory searches and XML-backed authentication. The skill guides checks for login bypasses and information leaks, uses control responses to reduce false positives, and distinguishes Active Directory from other LDAP directories.

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• Reviewing directory-backed corporate login
• Testing employee and address-book searches
• Checking authentication backed by XML data

LLM prompt injection playbook. Use when testing AI/LLM applications for direct injection, indirect injection via RAG/browsing, tool abuse, data exfiltration, MCP security risks, and defense bypass techniques.

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Harden AI/LLM deployments against prompt injection, data exfiltration, model theft, and supply chain attacks. Covers input validation, output filtering, access control, model API security, and compliance controls for production AI systems.

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Secure AI agents against prompt injection, tool abuse, and data exfiltration with defense-in-depth controls. Use when building, deploying, or hardening agentic AI systems that invoke tools, access data, or interact with production infrastructure.

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