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Start with the pressure: sales, launch, abuse, agents, data, or guardrails

Attack · Test · Validate

SecEng Attack

Test the AI abuse paths normal AppSec misses - then chain what matters.

Reproduce failures across prompts, retrieval, tools, agents, authorization, data boundaries, workflows, and AI code, then use Attack Path Chaining (APC) to determine which findings form meaningful multi-step attack paths.

SecEng Attack separates theoretical exposure from reproducible security failure. We validate abuse paths, capture evidence, and turn confirmed findings into replayable test cases. Where multiple findings combine into something larger, APC builds an evidence-grounded core, independently challenges the chain, models clearly labeled precedent-backed extensions where appropriate, and identifies the controls that break the most paths.

APC at a glance

From isolated findings to defensible attack chains.

Most security reports stop at individual findings. APC asks the harder question: which findings can actually combine into a meaningful adversary path?

1

Grounded core

Build only from target-, environment-, or system-specific evidence: findings, CVEs, exposed credentials, runtime evidence, code paths, authority relationships, and other supported security intelligence.

2

Precedent-backed extensions

Where direct evidence ends, model clearly labeled earlier or later TTP hypotheses using supported real-world adversary sequencing. Inference never becomes a grounded finding.

3

Independent validation

Separate validators challenge evidence grounding, ATT&CK mapping, sequence plausibility, and unsupported assumptions before the chain advances.

157Scenario files in the current registry
15Active attack packs
22Threat vectors represented
8First-class tool adapters

Capabilities

Four lenses. One attack picture.

No single test tells the whole story. Static code paths, adversarial behavior, delegated authority, and multi-step attack chaining answer different security questions. SecEng Attack combines those signals without pretending they are the same thing.

Code-derived attack paths

Find AI-specific source-to-sink paths involving prompts, retrieval, model calls, agents, MCP, tools, sensitive data, and downstream actions.

Adversarial reproduction

Validate direct and indirect prompt injection, retrieval abuse, jailbreaks, tenant leakage, unsafe tool use, policy bypass, multimodal abuse, and workflow manipulation with replayable evidence.

Authority abuse

Test whether agent permissions, credentials, tool combinations, approval gaps, and delegated actions create unsafe outcomes or excessive blast radius.

Attack Path Chaining (APC)

Connect target-, environment-, or system-specific findings into ATT&CK-mapped attack clusters. Keep evidence-grounded steps separate from precedent-backed hypotheses, independently validate the chain, and identify remediation chokepoints.

Confirmed reproduction

Prove whether the behavior is real. Every confirmed finding carries reproduction evidence and a retest condition.

RAG and XPIA validation

Test retrieval authorization, tenant and corpus boundaries, source provenance, poisoned or hostile retrieved content, and indirect prompt injection paths.

Guardrail and policy bypass

Evaluate instruction hierarchy, policy enforcement, refusal behavior, output controls, and adversarial framing under realistic failure conditions.

Replay and regression

Convert confirmed failures into reusable fixtures so the same path can be retested after remediation and across later releases.

Attack Path Chaining - APC

From isolated findings to defensible attack chains.

Most security reports stop at individual findings. APC asks the harder question: which findings can actually combine into a meaningful adversary path?

Grounded core

Build only from target-, environment-, or system-specific evidence: findings, CVEs, exposed credentials, runtime evidence, code paths, authority relationships, and other supported security intelligence.

Precedent-backed extensions

Where direct evidence ends, model clearly labeled earlier or later TTP hypotheses using supported real-world adversary sequencing. Inference never becomes a grounded finding.

Independent validation

Separate validators challenge evidence grounding, ATT&CK mapping, sequence plausibility, and unsupported assumptions before the chain advances.

What APC returns

validated attack clusters
grounded vs speculative step labels
MITRE ATT&CK mapping
Attack Flow output
ATT&CK Navigator layers
qualitative loss-exposure reasoning and risk drivers
ranked remediation chokepoints
retest conditions
explicit analyst-review status

Scope note

APC performs defensive attack-path analysis at the tactic-technique-procedure level. It does not generate exploit code, payloads, credential material, or step-by-step intrusion instructions.

Workbench capabilities that support Attack

Use the right lens before you claim the path.

Workbench capability

SecEng Code Scanner

Find AI-native code paths across prompts, retrieval, models, agents, MCP, tools, sensitive data, and downstream actions. Feed relevant paths into adversarial validation and APC when deeper chaining is warranted.

Workbench capability

SecEng Adversarial Range

157 adversarial scenarios across prompt injection, RAG abuse, jailbreaks, tool misuse, multimodal, and agentic attack surfaces. Confirmed failures can feed APC for multi-step attack-path analysis.

Workbench capability

SecEng Authority Graph

Map and test how agents, identities, credentials, MCP servers, tools, approval gates, and external effects compose. Authority findings can become grounded APC evidence when they contribute to a larger attack path.

Workbench capability

Attack Path Chaining (APC)

Build validated attack clusters from grounded evidence, then challenge the chain independently before it becomes a defended claim.

Workbench capability

SecEng RAG Test Harness

Validate retrieval authorization, corpus boundary enforcement, tenant isolation, XPIA attack chains, and regression fixtures for RAG-backed AI systems.

Workbench capability

SecEng Artifact Analyzer

Analyze Rust, Go, browser, and MCP artifacts for authority signals, capability exposure, and evidence-quality review outputs.

Service

AI Red Team & Adversarial Testing

Structured adversarial testing for prompt injection, jailbreaks, tool misuse, retrieval abuse, and unsafe workflows.

Framework coverage

Map findings to the frameworks teams already use.

Where applicable, findings and evidence can be mapped to OWASP LLM Top 10, MITRE ATLAS, NIST AI RMF, EU AI Act, ISO/IEC 42001, and other supported control frameworks.

OWASP LLM Top 10MITRE ATLASNIST AI RMFEU AI ActISO 42001