4️⃣ Why It Matters
AI security ≠ traditional cybersecurity.
The attack surface is language itself — intent, context, and semantics.
llm-security helps developers bridge that gap before LLMs reach production scale.

Defense starts with awareness.
#AISecurity #LLM #PromptSecurity #Rust

— Valh4x

3️⃣ Usage Example

let security = LLMSecurityLayer::new(LLMSecurityConfig::default());

// Sanitize before sending to LLM
let safe_code = security.sanitize_code_for_llm(user_code)?;

// Validate LLM response
security.validate_llm_output(llm_response)?;

Protect your AI from manipulation. 🔐
#RustCode

2️⃣ The Solution
llm-security protects both input and output channels.

✅ 90+ detection patterns
✅ Jailbreak prevention
✅ Unicode sanitization
✅ Output validation
✅ Semantic cloaking detection
✅ Secure prompt generation

Integrate directly with your Rust AI pipelines
#LLMSecurity #Protection #Rust #AI

1️⃣ The Problem
LLMs can be tricked, manipulated, or socially engineered.
Common exploits include:
• “Ignore previous instructions” injections
• Jailbreak prompts (DAN, STAN, etc.)
• Persuasive social engineering
• Output poisoning or malicious instructions

#PromptInjection #Jailbreaking #AISecurity

3️⃣ Usage Example

let mut engine = ThreatIntelEngine::new(ThreatIntelConfig::default());
engine.initialize().await?;

let vulns = engine.query_vulnerabilities("apache", "2.4").await?;
let assessment = engine.assess_risk(&vulns);

Unify your threat intelligence
#Security #ThreatIntelligence

— Valh4x

2️⃣ The Solution
Unified access to threat data —
✅ Multi-source aggregation
✅ 4 authentication modes
✅ Retry with exponential backoff
✅ Risk assessment & failure isolation
✅ Capability-based queries

Reliable intel from one interface.
#ThreatIntel #Rust #Automation

1️⃣ The Problem
Threat intel aggregation is messy — multiple sources, formats, and auth schemes.
JSON, XML, STIX, TAXII… every feed looks different, updates at different times, and breaks your parser.

threat-intel unifies this chaos.
#ThreatIntel #RustLang #CyberSecurity

3️⃣ Usage Example

use path_security::validate_path;
let safe = validate_path(user_input, base_dir)?;

Blocks encoding, Unicode, and NTFS tricks before they bite. 🔐

— Valh4x

2️⃣ The Solution
path-security neutralizes all known traversal techniques.
Zero dependencies (only anyhow), production-tested, cross-platform.

Perfect for web uploads, archives, and git operations.
#RustLang #CrossPlatform #ProductionReady

1️⃣ The Problem
Most validators just block ../, missing dozens of bypasses:
• %2e%2e%2f, %252e%252e%252f
• UTF-8 overlong bytes
• Unicode homoglyphs (․․/)
• NTFS alternate data streams

#AttackVectors #Unicode #Windows #SecurityResearch

3️⃣ Quick start

let shield = QuantumShield::new();
let (pub, priv) = shield.generate_keypair().await?;
let encrypted = shield.encrypt_hybrid(&data, &pub).await?;

Stay ahead of the curve. ⚡

— Valh4x

2️⃣ Built for the real world
6+ months in production.
Zero unnecessary deps.
Memory-safe, auto-zeroized, cross-platform.
Works on blockchain, IoT, and air-gapped systems.

1️⃣ Why it matters
RSA and ECDSA won’t survive quantum decryption.
quantum-shield uses Kyber (KEM) + Dilithium (signatures) with hybrid encryption — classical + quantum strength.