AWESOME-LLMSECOPS

Elevating LLM Security with Operational Excellence.

LLMSecOps: Securing Large Language Model Operations

LLMSecOps (Large Language Model Security Operations) is an emerging discipline focused on integrating security best practices throughout the lifecycle of LLMs—mirroring how DevSecOps brings security into every stage of software development. LLMSecOps aims to ensure that LLMs are robust, trustworthy, and resilient against evolving threats, from data collection and model development to deployment, monitoring, and decommissioning.

Key Goals of LLMSecOps

• Protect LLM systems from data poisoning, model theft, prompt injection, adversarial attacks, and supply chain vulnerabilities

• Ensure trust, compliance, and auditability in LLM models and their outputs

• Embed security checks and continuous monitoring throughout the LLM pipeline, including CI/CD, deployment, and inference

Table of Contents

• llm 3 types of models
• Types of LLMs
  • Autoregressive Models
  • Autoencoding Models
  • Sequence-to-Sequence (Seq2Seq) Models
• Architecture risks
• OWASP
• LLMSecOps Life Cycle
  • Key Stages of the LLMSecOps Lifecycle
• Tools Attack and defense
  • Attack
  • Defense
• Monitoring
• Books
• Blogs
• Articles
• Social networks
• Contributing

llm 3 types of models

Large Language Models (LLMs) can be categorized into three main types based on their underlying architecture and primary use cases: Autoregressive Models, Autoencoding Models, and Sequence-to-Sequence (Seq2Seq) Models. Here’s a clear overview of each:

Types of LLMs

Type	Description	Example Models	Best For
Autoregressive Models	Generate text one token at a time by predicting the next token based on previous ones.	GPT series, Claude	Text generation, chatbots, creative writing
Autoencoding Models	Learn to understand context by reconstructing masked parts of input text (masked language model).	BERT, RoBERTa	Text understanding, classification, NER
Sequence-to-Sequence Models	Transform one sequence into another, often using encoder-decoder architecture.	T5, BART, MarianMT	Translation, summarization, Q&A

Autoregressive Models

• How they work: Predict the next word in a sequence given the previous words, generating text left-to-right.
• Strengths: Excellent at generating fluent, coherent text.
• Limitations: May lose coherence in long outputs, can hallucinate facts.
• Examples: GPT-2, GPT-3, GPT-4, Claude.

Autoencoding Models

• How they work: Mask parts of the input and train the model to reconstruct the missing parts using context from both sides.
• Strengths: Superior at understanding language, context, and meaning.
• Limitations: Not designed for generating long, coherent text.
• Examples: BERT, RoBERTa, ALBERT.

Sequence-to-Sequence (Seq2Seq) Models

• How they work: Use an encoder to process the input sequence and a decoder to generate the output sequence.
• Strengths: Ideal for tasks where input and output are both sequences, such as translation or summarization.
• Limitations: Require more complex training and fine-tuning.
• Examples: T5, BART, MarianMT.

These three types form the foundation of most modern LLMs, and many advanced models combine elements from each to achieve better performance across a wider range of tasks.

Architecture risks

Risk	Description
Recursive Pollution	LLMs can produce incorrect output with high confidence. If such output is used in training data, it can cause future LLMs to be trained on polluted data, creating a feedback loop problem.
Data Debt	LLMs rely on massive datasets, often too large to thoroughly vet. This lack of transparency and control over data quality presents a significant risk.
Black Box Opacity	Many critical components of LLMs are hidden in a "black box" controlled by foundation model providers, making it difficult for users to manage and mitigate risks effectively.
Prompt Manipulation	Manipulating the input prompts can lead to unstable and unpredictable LLM behavior. This risk is similar to adversarial inputs in other ML systems.
Poison in the Data	Training data can be contaminated intentionally or unintentionally, leading to compromised model integrity. This is especially problematic given the size and scope of data used in LLMs.
Reproducibility Economics	The high cost of training LLMs limits reproducibility and independent verification, leading to a reliance on commercial entities and potentially unreviewed models.
Model Trustworthiness	The inherent stochastic nature of LLMs and their lack of true understanding can make their output unreliable. This raises questions about whether they should be trusted in critical applications.
Encoding Integrity	Data is often processed and re-represented in ways that can introduce bias and other issues. This is particularly challenging with LLMs due to their unsupervised learning nature.

OWASP

Title	Link
OWASP Top 10 for LLM Applications 2025	https://owasp.org/www-project-top-10-for-large-language-model-applications/assets/PDF/OWASP-Top-10-for-LLMs-v2025.pdf
OWASP Top 10 for Large Language Model Applications	https://owasp.org/www-project-top-10-for-large-language-model-applications/

LLMSecOps Life Cycle

LLMSecOps (Large Language Model Security Operations) refers to the integration of security practices throughout the entire lifecycle of large language models (LLMs), ensuring that these AI systems are robust, trustworthy, and resilient to threats. This approach is an evolution of MLOps, inspired by DevSecOps, and is designed to address the unique risks and challenges associated with deploying and maintaining LLMs and other AI models.

Key Stages of the LLMSecOps Lifecycle

The LLMSecOps life cycle typically covers the following stages, each with specific security considerations:

Stage	Description & Security Focus
1. Object Specification	Define the business problem, objectives, and KPIs. Integrate security and compliance requirements from the outset.
2. Data Acquisition & Storage	Securely source, store, and manage datasets. Ensure data privacy, provenance, and compliance with regulations. Scan for toxic, sensitive, or proprietary data.
3. Data Curation & Tracking	Organize and normalize data. Maintain detailed lineage and access controls to prevent unauthorized use or leakage.
4. Model Training	Train models in secure environments. Protect against data poisoning, adversarial attacks, and ensure reproducibility.
5. Model Acceptance Testing	Verify model quality, explainability, and robustness. Perform security audits and adversarial testing to detect vulnerabilities.
6. Model Deployment	Package and deploy models securely. Implement CI/CD with security checks, and validate models before production.
7. Model Monitoring & Control	Continuously monitor models for performance, drift, and anomalous behaviors. Detect and respond to threats, retrain as needed.

Tools Attack and defense

Attack

Tools Name	Description	Link	Stars
Garak	An advanced open-source vulnerability scanner developed by NVIDIA for large language models (LLMs). Garak enables comprehensive AI red teaming and automated vulnerability assessments, helping developers identify, simulate, and mitigate a wide range of security weaknesses in LLM applications, including prompt injection, jailbreaks, and other adversarial attacks. It is designed to strengthen LLM security across various deployment scenarios.	https://github.com/NVIDIA/garak
ps-fuzz 2	An interactive open-source tool that helps developers test and harden the system prompts of their generative AI applications. ps-fuzz 2 (Prompt Fuzzer) automatically runs dynamic LLM-based attacks, evaluates the system’s resilience, and provides actionable security scores, enabling iterative improvement of prompt safety and robustness against injection and manipulation attacks.	https://github.com/prompt-security/ps-fuzz
LLMmap	A research-driven tool that introduces a novel fingerprinting technique for large language models. LLMmap actively identifies and distinguishes between specific LLM versions within integrated applications by sending curated queries and analyzing model responses. Its approach combines strategic querying and machine learning-based inference to accurately classify and attribute LLMs, aiding in model provenance and security analysis.	https://github.com/pasquini-dario/LLMmap
Agentic Security	An open-source vulnerability scanner focused on agent workflows and LLMs. Agentic Security protects AI systems from sophisticated threats including jailbreaks, multimodal attacks (text, image, audio), multi-step attacks, and fuzzing. It supports API integration, reinforcement learning-based attacks, and stress testing, enabling comprehensive security evaluations of both standalone LLMs and agent-based AI systems.	https://github.com/msoedov/agentic_security
Mindgard CLI	A continuous, automated red teaming platform that integrates with Mindgard’s security suite to identify, triage, and remediate security risks in AI models. Mindgard CLI covers a broad spectrum of threats—including jailbreaks, prompt injection, model inversion, extraction, poisoning, evasion, and membership inference—and enables ongoing security posture tracking within MLOps pipelines and across model lifecycles.	https://github.com/Mindgard/cli
LLM confidentiality	A tool designed to ensure confidentiality and privacy in LLM-powered applications. It detects potential data exposure risks, summarizes privacy policies, and provides visualization dashboards for data flow and policy compliance. The tool helps users and organizations manage confidential data, prevent leaks, and make informed decisions about data handling when interacting with LLM-based systems.	https://github.com/LostOxygen/llm-confidentiality

Defense

Tools Name	Description	Link	Stars
Purple Llama	A comprehensive open-source suite by Meta to evaluate, improve, and strengthen the security of large language models (LLMs), including prompt injection, jailbreak, and data leakage detection tools.	https://github.com/meta-llama/PurpleLlama
Rebuff	API specialized in detecting prompt injection attacks and preventing sensitive information leaks, using built-in rules and canary word mechanisms.	https://github.com/protectai/rebuff
LLM Guard	Self-hostable tool offering various prompt and output scanners to identify security risks such as injections, leaks, toxic content, and sensitive information.	https://github.com/laiyer-ai/llm-guard
NeMo Guardrails	NVIDIA’s framework for defining customizable rules to protect LLM applications against jailbreaks, hallucinations, and other undesired behaviors, easily integrated into workflows.	https://github.com/NVIDIA/NeMo-Guardrails
Vigil	Security solution for LLMs offering local or Dockerized deployment, leveraging proprietary HuggingFace datasets to efficiently detect threats and vulnerabilities in prompts.	https://github.com/deadbits/vigil-llm
LangKit	Library providing ready-to-use functions for detecting jailbreaks, prompt injections, and sensitive information in LLM inputs or outputs.	https://github.com/whylabs/langkit
GuardRails AI	Tool focused on automatically detecting secrets and confidential information in LLM-generated responses, with advanced validation and filtering features.	https://github.com/ShreyaR/guardrails
Hyperion Alpha	HuggingFace model designed to effectively detect prompt injection attempts and jailbreak attacks in LLM conversations.	https://huggingface.co/Epivolis/Hyperion	N/A
LLM-Guard	Open-source tool for securing LLM interactions by detecting and blocking malicious prompts, data leaks, and sensitive content.	https://github.com/protectai/llm-guard
Whistleblower	Monitoring and prevention solution for LLM vulnerabilities, capable of identifying and blocking known attacks and reporting suspicious AI behaviors.	https://github.com/Repello-AI/whistleblower
Plexiglass	Security tool for LLM applications, providing modules for prompt injection detection, jailbreak prevention, and risky content filtering.	https://github.com/safellama/plexiglass
Prompt Injection Defenses	A collection of rules and best practices to protect LLMs against prompt injection attacks, easily integrable into text generation pipelines.	https://github.com/tldrsec/prompt-injection-defenses
LLM Data Protector	Suite of tools for protecting data in LLM-based chatbots, preventing information leaks and enhancing the privacy of user interactions.	https://ai.raftds.ru/security/#	N/A
Gen AI & LLM Security for developers: Prompt attack mitigations on gemeni	Practical notebook by Google showcasing strategies for mitigating prompt attacks on Gemini, with real-world use cases for developers.	https://github.com/GoogleCloudPlatform/generative-ai/blob/main/gemini/responsible-ai/gemini_prompt_attacks_mitigation_examples.ipynb
Promptfoo	Promptfoo is an open-source, developer-friendly tool for evaluating, testing, and red teaming LLM applications	https://github.com/promptfoo/promptfoo

Monitoring

Langfuse is an open-source LLM engineering platform designed to help teams monitor, trace, and improve the security and reliability of their LLM-based applications. It provides both real-time and asynchronous tools for detecting, evaluating, and responding to security risks in LLM workflows.

Tool	Link
Langfuse	https://langfuse.com/

Books

Title	Author(s)	Link
The Developer's Playbook for Large Language Model Security	Steve Wilson	https://www.oreilly.com/library/view/the-developers-playbook/9781098162191/
Generative AI Security: Theories and Practices (Future of Business and Finance)	Ken Huang, Yang Wang, Ben Goertzel, Yale Li, Sean Wright, Jyoti Ponnapalli	https://www.amazon.com/Generative-AI-Security-Theories-Practices/dp/3031542517
Adversarial AI Attacks, Mitigations, and Defense Strategies: A cybersecurity professional's guide to AI attacks, threat modeling, and securing AI with MLSecOps	John Sotiropoulos	https://www.packtpub.com/en-us/product/adversarial-ai-attacks-mitigations-and-defense-strategies-9781835087985

Blogs

Title	Link
Embrace The Red	https://embracethered.com/blog/
Kai's Blog	https://kai-greshake.de/
LLM Security	https://llmsecurity.net/
AI safety takes	https://newsletter.danielpaleka.com/
hackstery	https://hackstery.com/posts/

Social networks

Title	Link
LLM Security	https://x.com/llm_sec
OWASP GenAI Security Project - Top 10 For LLM Apps	https://x.com/LLM_Top10
AI Village @ DEF CON	https://x.com/aivillage_dc
Pliny the Liberator	https://x.com/elder_plinius/
Telegram: llmsecurity	https://t.me/llmsecurity