Exploit misconfigurations in AWS Cognito, specifically focusing on unauthorized identity pool access, user pool self-registration issues, and privilege escalation via custom attributes to access broader AWS infrastructure.
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Code obfuscation analysis and deobfuscation playbook. Use when reversing binaries protected by junk code, opaque predicates, self-modifying code, control flow flattening, VM protection, or string encryption.
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OS command injection occurs when user input is passed unsanitized to a system shell via dangerous APIs: Java `Runtime.exec()`, Python `os.system/subprocess`, PHP `system/shell_exec/exec/proc_open`, C `system/exec`. Detect via pipe `|`, semicolon `;`, `&&`, `||`, backtick, `$()` operators, and time-delay payloads (`sleep 5`). Tools: Commix, Burp Suite, OWASP WebGoat.
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Identify and exploit Cross-Origin Resource Sharing (CORS) misconfigurations. Use this skill when auditing APIs or web applications that share sensitive data across domains, forcing victims' browsers to inadvertently leak private information (e.g., API keys, PII, CSRF tokens) to an attacker-controlled website.
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CORS misconfiguration allows attacker-controlled origins to read sensitive cross-origin responses when servers echo the `Origin` header in `Access-Control-Allow-Origin` or set it to `*` with `Access-Control-Allow-Credentials: true`. Detect via `Origin: https://attacker.com` reflection in `Access-Control-Allow-Origin` response header, wildcard `*` on credentialed endpoints, and null origin acceptance. Tools: OWASP ZAP, Burp Suite, manual `fetch()` with `credentials: include`.
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CORS misconfiguration testing playbook. Use when analyzing cross-origin trust, credentialed browser reads, origin reflection, preflight policy bugs, and browser-based access to authenticated APIs.
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Audit and attack session cookies via missing Secure/HttpOnly/SameSite attributes, overly broad Domain/Path scope, non-expiring persistent cookies, absent __Host- and __Secure- prefixes, browser cache leakage (Cache-Control: no-store missing), session token predictability via Burp Sequencer analysis, server-side session not invalidated on logout, and SSO single-logout bypass. Tools: Burp Suite Repeater/Sequencer, OWASP ZAP, EditThisCookie, Tamper Data, Cookiebro.
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Container and Kubernetes security assessment. Tests container escape vectors, Docker/containerd socket exposure, K8s RBAC misconfigurations, pod security violations, exposed API servers, etcd access, service account token abuse, image layer secrets, private registry attacks, SSRF to metadata services, cross-namespace network bypass, CIS benchmarks, crypto miner detection, resource exhaustion, and admission controller gaps. Both external and internal (compromised pod) perspectives. Uses trivy, kube-bench, kubectl, nuclei, dive, amicontained, and docker-bench-security. Covers OWASP Kubernetes Top 10 and all 22 Kubernetes Goat attack scenarios.
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Container escape playbook. Use when operating inside a Docker container, LXC, or Kubernetes pod and need to escape to the host via privileged mode, capabilities, Docker socket, cgroup abuse, namespace tricks, or runtime vulnerabilities.
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Full ASVS 5.0 compliance assessment against a codebase and/or architecture diagrams. Reads all 346 controls from the companion CSV, performs targeted code analysis per control, and produces a complete matrix marked COMPLIANT / NON_COMPLIANT / NOT_RELEVANT — with per-control reasoning and evidence (code snippets, file:line references, diagram observations). Outputs a reviewed CSV matrix and a self-contained HTML evidence report.
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Triages an Aikido security findings CSV against a local codebase. For each finding, reads the flagged file, traces the code path, and verdicts it as KEEP OPEN or CLOSE with a specific reason. Outputs a reviewed CSV and a self-contained HTML evidence report. Run this at the end of a pentest when an Aikido CSV is available.
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AI/LLM red-team assessment using the OWASP LLM Top 10 (2025) + OWASP AI Testing Guide (AITG v1, Nov 2025) frameworks, plus OWASP MCP Top 10 runtime testing for agentic/MCP targets. Tests prompt injection, jailbreaks, system prompt leakage, sensitive data extraction, excessive agency, improper output handling, model extraction, content bias, evasion, membership inference, MCP token exposure, MCP command injection, and more. Uses four tools in combination: FuzzyAI (single-turn jailbreak fuzzing), PyRIT (multi-turn orchestrated attacks), Garak (probe-based vulnerability scanning), and promptfoo (plugin-based red-team evaluation). Each tool covers different OWASP categories; running them together gives systematic coverage. Includes a conditional MCP reconnaissance phase and a post-access AI infrastructure phase (chained from /post-exploit). Produces: OWASP LLM Top 10 + AITG + MCP coverage matrix, findings per category, architecture diagram of the AI system, PoCs for confirmed exploits. Chains into /gh-export for issue filing.
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Systematically extract hidden system prompts, core directives, and invisible context intentionally concealed within Large Language Model (LLM) applications. This skill utilizes targeted linguistic engineering and boundary manipulation to bypass prompt opacity.
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Configure Claude as a "Pair Hunter" — autonomous overnight hacking, context management via per-target .claudemd files, sub-agent compaction avoidance, and scope enforcement. Based on Critical Thinking Bug Bounty Podcast Episode 166.
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AI/ML security playbook. Use when assessing model supply chain attacks (pickle RCE, poisoned weights), adversarial examples, model poisoning, model stealing, data privacy attacks (membership inference, model inversion), and autonomous agent security risks.
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Advanced techniques for bypassing LLM safety filters, instruction tuning, and system prompt restrictions using specialized linguistic constructs, hypothetical scenarios, and persona adoption.
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Execute sophisticated Prompt Injection and Jailbreak techniques against Large Language Models (LLMs) to bypass safety filters, extract system prompts, and manipulate the AI's output to perform malicious or disallowed actions.
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Bypass AI safety filters by encoding malicious prompts using ciphers and obfuscation techniques (e.g., Base64, ROT13, Leetspeak, Morse code). This skill exploits the gap where the LLM can decode the request, but intermediate keyword-based safety classifiers cannot.
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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.
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Identify and simulate Data Poisoning attacks aimed at degrading or skewing an AI model's accuracy. This skill focuses on Adversarial Machine Learning concepts where attackers inject malicious or mislabelled data points into training or fine-tuning datasets (e.g., feedback loops) to bias the AI.
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Test AI agent systems for tool abuse, unauthorized actions, privilege escalation through tool chaining, and safety bypass via agentic workflows. Use this skill when assessing autonomous AI agents that use tool-calling (function calling, plugins, actions) to interact with external systems. Covers multi-step attack chains, implicit trust exploitation, and capability boundary testing for AI agents.
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Execute advanced SQL Injection attacks to bypass WAFs and extract data from complex architectures. Use this skill for Boolean-Based Blind, Time-Based Blind, Second-Order SQLi, and Out-of-Band (OOB) SQLi across MySQL, PostgreSQL, MSSQL, and Oracle.
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Exploit Active Directory environments using Pass-the-Hash (PtH). This skill details how to authenticate to remote systems using the NTLM hash of a user's password instead of the plaintext password, enabling lateral movement and privilege escalation without cracking hashes.
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Execute AS-REP Roasting to extract and crack the NTLM hashes of Active Directory user accounts that have the "Do not require Kerberos preauthentication" flag explicitly enabled. This attack generates a recoverable Ticket Granting Ticket (TGT) without requiring the attacker to authenticate first.
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