Results for “injection”

Comprehensive CI/CD pipeline exploitation methodology covering GitHub Actions injection vectors (expression injection via PR titles and issue bodies, workflow_run event abuse, GITHUB_TOKEN over-scoping, composite action supply chain compromise), Jenkins attack paths (Groovy sandbox escapes, script console remote code execution, Java remoting deserialization, credential store dumping, shared library injection), GitLab CI exploitation (YAML anchor injection, runner registration token abuse, CI variable extraction, protected branch bypass via merge request pipelines), and Azure DevOps pipeline agent compromise with service connection theft. Includes artifact poisoning techniques across all platforms, tooling guidance for gato and jenkins-attack-framework, and maps to MITRE ATT&CK T1195.002 (Supply Chain Compromise: Compromise Software Supply Chain). Covers enumeration of pipeline configurations, privilege escalation from contributor to code execution, lateral movement through pipeline trust boundaries, and p...

The English summary is being prepared. Showing the original description.

Assess the security of LLM applications and AI agents by tracing inputs, retrieved content, tool calls, and outputs. Covers prompt injection, where content carries unwanted instructions, along with permission misuse, memory poisoning, and supply-chain risks.

• Testing chatbot resistance to prompt injection
• Auditing agent tool permissions and approval steps
• Checking retrieval access controls and memory poisoning

Review GitHub Actions workflows for unsafe input handling, excessive token permissions, exposed secrets, and changeable action references. Get findings grouped by issue type, with severity ratings, exact YAML locations, and concrete fixes to review.

• Reviewing CI workflows for security issues
• Checking privileged triggers that run fork code
• Restricting GITHUB_TOKEN permissions

Monitor Modbus traffic in industrial control networks to identify unauthorized writes, unusual commands, and departures from normal communication. Use packet analysis and intrusion detection rules to investigate suspected controller changes and produce reports with recommended actions.

• Investigating unauthorized changes to PLC registers
• Setting up Modbus intrusion detection rules
• Checking communication patterns after a suspected compromise

Add checks to LLM prompts and responses using Llama Guard 3, NeMo Guardrails, and LLM Guard. Configure content screening, conversation boundaries, and sensitive-data filtering, then measure blocking and false-positive rates with labeled test prompts.

• Adding runtime safety checks to LLM, RAG, or agent apps
• Screening prompts for injection and jailbreak attempts
• Filtering sensitive or toxic model responses

Plan controlled failure experiments to uncover system weaknesses. Define normal operating metrics, estimate affected users and reliability risk, set abort and recovery conditions, and turn experiment results into a structured review with follow-up actions.

• Designing a controlled failure experiment
• Estimating affected users and reliability risk
• Planning a team failure response exercise

Assess security risks in AI systems by scanning prompts for known attack patterns and scoring data exposure, poisoning, and tool abuse risks. Map findings to MITRE ATLAS, an AI threat framework, and identify protective controls.

• Scan application prompts for injection and jailbreak patterns
• Review malicious instructions in retrieved documents
• Assess training data exposure and poisoning risks

Review source code, dependencies, and infrastructure settings for security issues. Find exposed credentials, unsafe input handling, and access control gaps, then organize findings into a report with severity levels, affected locations, and recommended fixes.

• Review code security before deployment
• Find API keys and passwords embedded in files
• Check dependencies for known vulnerabilities

Audits GitHub Actions workflows that run AI coding agents. Traces how attacker-controlled content reaches prompts, checks permissions and sandbox settings, and reports evidence, severity, and fixes for prompt injection and related configuration risks.

• Reviewing workflows that run AI coding agents
• Tracing issue and PR content into agent prompts
• Checking sandbox settings and user allowlists

Plan persistence testing for authorized Windows and Linux security exercises, examining how access can survive logons or restarts. The skill compares mechanisms by privilege level and environment, with validation steps, detection indicators, and cleanup considerations.

• Planning persistence tests for authorized exercises
• Comparing methods by operating system and privilege
• Reviewing detection indicators for persistence

Assess weaknesses in the software supply chain, from dependency packages to build pipelines and distributed artifacts. Covers authorized proof-of-concept testing, package confusion, container image risks, signing abuse, and defensive checks for tampering.

• Assess dependency confusion risks in package registry settings
• Review tampering risks in CI/CD artifacts and container images
• Plan a non-destructive proof of concept and remediation report

Assess server-side template injection, where user input is interpreted as template code. Identify template engines, evaluate information exposure and code execution risks, and organize evidence alongside detection and prevention measures.

• Assessing template rendering endpoints
• Identifying the template engine in use
• Evaluating information exposure and code execution risks

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.

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

Audit MCP servers, which connect AI agents to external tools, for injected instructions, excessive access, secret leaks, and unapproved actions. Trace untrusted input through tool calls and document demonstrated issues with concrete fixes.

• Auditing a custom MCP server
• Reviewing third-party tool integrations
• Checking client connection settings

Investigate cross-site scripting, where untrusted input executes as JavaScript in a web page. Check reflected, stored, and browser-side cases, distinguish escaped output from executable content, and validate reproducible behavior and user impact before reporting.

• Checking how search terms and URL parameters appear in pages
• Testing stored content in wikis and profile fields
• Reviewing JavaScript paths that insert input into pages

Investigate server-side template injection, where user input is interpreted as template code. The skill covers engine identification, testing email and PDF rendering, and checking whether an injection can lead to command execution on the server.

• Assessing email template rendering
• Testing PDF and report generators
• Reviewing authenticated CMS template editors

Assess Node.js applications for unsafe input handling and configuration weaknesses. The skill covers prototype pollution, where shared object properties are altered, plus template and command injection, proxy trust issues, and file exposure, with checks for observable impact.

• Investigate prototype pollution in Node.js APIs
• Check Express proxy trust and IP restrictions
• Review template rendering and command execution inputs

Scan projects for known dependency vulnerabilities, exposed credentials, and risky code patterns such as SQL injection. Produces text or JSON reports with file locations and severity levels, with options to run individual checks.

• Checking dependency vulnerabilities before release
• Finding hardcoded API keys and passwords
• Reviewing code for common security weaknesses

Solve CTF web challenges using concrete test inputs, request templates, and PHP code review checklists. The reference covers type comparisons, command injection, template injection, deserialization, source extraction, and common locations for challenge flags.

• Review PHP source code in a CTF challenge
• Investigate type comparisons and input filters
• Explore template injection and deserialization

Guide authorized tests for server-side template injection, where input is evaluated as template code. This skill defines investigation boundaries, alternative checks, and the evidence needed to support findings across engines such as Jinja2 and Twig.

• Define the scope of an authorized template injection test
• Review template execution evidence and engine identification
• Consider other checks when template tests stall

Java "Ghost Bits" / Cast Attack playbook (Black Hat Asia 2026). Use when attacking Java services where 16-bit char is silently narrowed to 8-bit byte to bypass WAF/IDS for SQL injection, deserialization RCE, file upload (Webshell), path traversal, CRLF injection, request smuggling, and SMTP injection. Affects Tomcat, Spring, Jetty, Undertow, Vert.x, Jackson, Fastjson, Apache Commons BCEL, Apache HttpClient, Angus Mail, JDK HttpServer, Lettuce, Jodd, XMLWriter and re-enables many "patched" CVEs through WAF bypass.

The English summary is being prepared. Showing the original description.

Email header injection and spoofing playbook. Use when testing contact forms, email APIs, password reset flows, or any feature that constructs SMTP messages with user-controlled fields. Covers CRLF injection in headers, SPF/DKIM/DMARC bypass, and phishing amplification.

The English summary is being prepared. Showing the original description.

Dangling markup injection playbook. Use when HTML injection is possible but JavaScript execution is blocked (CSP, sanitizer strips event handlers, WAF blocks script tags) — exfiltrate CSRF tokens, session data, and page content by injecting unclosed HTML tags that capture subsequent page content.

The English summary is being prepared. Showing the original description.

Detect GraphQL injection vulnerabilities in a codebase using a three-phase approach: recon (confirm GraphQL usage and find unsafe operation document assembly sites), batched verify (trace user input to those sites in parallel subagents, up to 3 candidate sites each), and merge (consolidate batch results). Requires sast/architecture.md (run sast-analysis first). Outputs findings to sast/graphql-results.md. If no GraphQL technology is found in Phase 1, later phases are skipped. Use when asked to find GraphQL injection, unsafe GraphQL document construction, or operation string injection bugs.

The English summary is being prepared. Showing the original description.