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Exact duplicates and conflicting IDs need different treatment.
CSV Exact Audit is now available: a local Python toolkit for analysts and freelancers who need a repeatable check on CSV exports.
The synthetic demo has 12 input records: 5 in cleaned.csv, 5 flagged for review, and 2 exact duplicates logged separately. Leading zeros are preserved as cell strings. Conflicting or blank IDs are flagged for review; values are not guessed or silently merged.
Included: reusable Python source, run instructions, tests, synthetic examples, three result CSVs, a JSON audit, and an original-byte backup. Processing stays on your machine; no API key or extra packages are needed.
$19 one-time. Requires Python 3.10+ and a terminal. Verified on macOS; Windows and Linux are not natively tested for this release. Scope is one UTF-8 CSV at a time, exact duplicates and optional ID review.
AI assisted code development and testing. The example is synthetic, not customer data.
See the scope and download details:
https://contra.com/products/hXKUWINO-csv-exact-audit-local-duplicate-and-id-review-toolkit
Product page, requirements and download details: CSV Exact Audit — $19 one-time
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This project focused on building and testing a practical AI security assessment lab for evaluating LLM defenses against prompt injection and jailbreak attacks.
I integrated Spikee by Reversec with a locally hosted cybersecurity model running through LM Studio, then added NVIDIA NeMo Guardrails to compare model behavior under three conditions: no guardrails, input filtering, and combined input/output protection.
The work included configuring the local model environment, building a custom FastAPI gateway, integrating NeMo Guardrails, troubleshooting model latency and timeout issues, creating a reusable Spikee target, and analyzing attack results using Spikee’s built-in reporting tools.
The project also explored different adversarial testing approaches, including prompt injection datasets, obfuscation, encoded attacks, Best-of-N testing, synthetic canary leakage tests, and structured benchmark comparisons.
The objective was to measure how much the guardrails reduced successful attacks while keeping the model, dataset, and testing conditions consistent.
Tools used: Spikee, NVIDIA NeMo Guardrails, LM Studio, Python, FastAPI, PowerShell, Parrot OS, local LLMs, JSONL datasets, and custom security testing scripts.
This project demonstrates a hands-on approach to LLM red teaming, AI safety testing, prompt-injection assessment, and guardrail validation for organizations deploying generative AI systems.
pretty cool tbh. testing the same attacks with different guardrail setups makes the results way more interesting.
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𝐌𝐲 𝐫𝐨𝐥𝐞: Python Backend Engineer focused on AWS serverless architecture
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𝐄𝐧𝐭𝐞𝐫𝐩𝐫𝐢𝐬𝐞 𝐈𝐝𝐞𝐧𝐭𝐢𝐭𝐲 & 𝐀𝐜𝐜𝐞𝐬𝐬 𝐌𝐚𝐧𝐚𝐠𝐞𝐦𝐞𝐧𝐭 𝐏𝐨𝐫𝐭𝐚𝐥 | 𝐒𝐒𝐎, 𝐑𝐁𝐀𝐂 & 𝐌𝐅𝐀
𝐌𝐲 𝐫𝐨𝐥𝐞: Backend Engineer / Flask Developer
𝐏𝐫𝐨𝐣𝐞𝐜𝐭 𝐝𝐞𝐬𝐜𝐫𝐢𝐩𝐭𝐢𝐨𝐧:
I integrated Single Sign-On (SSO) using OpenID Connect into an existing Flask application. I implemented a secure authentication flow with a standard identity provider, enabling seamless and centralized login. I handled token validation, session management, and secure user identity processing. This reduced login friction while maintaining strong security standards, and I delivered a stable, production-ready authentication system within a tight deadline.
