Born ML Framework is currently in initial release. We provide security updates for the following versions:

Future releases will follow semantic versioning with security backports for major versions.

We take security seriously. If you discover a security vulnerability in Born ML Framework, please report it responsibly.

DO NOT open a public GitHub issue for security vulnerabilities.

Instead, please report security issues by:

1. Private Security Advisory (preferred): https://github.com/born-ml/born/security/advisories/new

2. Direct contact to maintainers: Create a private GitHub issue or contact via discussions

Please include the following information in your report:

• Description of the vulnerability
• Steps to reproduce the issue (include minimal code example)
• Affected versions (which versions are impacted)
• Potential impact (DoS, memory corruption, gradient manipulation, etc.)
• Suggested fix (if you have one)
• Your contact information (for follow-up questions)

• Initial Response: Within 48-72 hours
• Triage & Assessment: Within 1 week
• Fix & Disclosure: Coordinated with reporter

We aim to:

1. Acknowledge receipt within 72 hours
2. Provide an initial assessment within 1 week
3. Work with you on a coordinated disclosure timeline
4. Credit you in the security advisory (unless you prefer to remain anonymous)

Born ML Framework processes numerical data and executes tensor operations. This introduces specific security risks.

Risk: Unsafe memory access in tensor operations can lead to crashes or memory corruption.

Attack Vectors:

• Integer overflow in shape calculations or strides
• Buffer overflow when reading/writing tensor data
• Out-of-bounds access in indexing operations
• Memory exhaustion via massive tensor allocations

Mitigation in Library:

• ✅ Bounds checking on all tensor operations
• ✅ Shape validation before memory allocation
• ✅ Stride calculation with overflow detection
• ✅ Safe indexing with panic recovery
• 🔄 Fuzzing and property-based testing (planned for v0.2.0)

User Recommendations:

// ❌ BAD - Don't trust user-provided shapes without validation
tensor := born.Zeros(userProvidedShape, backend)

// ✅ GOOD - Validate shapes and sizes first
if !isValidShape(userShape) || totalSize(userShape) > maxAllowedSize {
    return errors.New("invalid tensor shape")
}
tensor := born.Zeros(validatedShape, backend)

Risk: Malicious inputs can exploit model weaknesses or cause unexpected behavior.

Attack Vectors:

• Adversarial examples causing misclassification
• Input shape mismatch causing panics
• NaN/Inf injection causing gradient explosions
• Extremely large values causing overflow

Mitigation:

• ✅ Input validation in NN modules
• ✅ Shape compatibility checks
• ✅ NaN/Inf detection in gradient computation
• 🔄 Input sanitization utilities (planned)

User Best Practices:

// ✅ Validate input shapes
if !input.Shape().Equal(model.ExpectedInputShape()) {
    return errors.New("invalid input shape")
}

// ✅ Check for NaN/Inf
if containsNaN(input) || containsInf(input) {
    return errors.New("invalid input values")
}

// ✅ Clip input ranges
input = clip(input, -10.0, 10.0)

Risk: Gradient computation can encounter numerical instability or trigger vulnerabilities.

Attack Vectors:

• Gradient explosion (unbounded gradients)
• Gradient vanishing (underflow to zero)
• NaN propagation in backward pass
• Circular gradient graphs causing infinite loops

Mitigation:

• ✅ Gradient clipping support in optimizers
• ✅ NaN detection in backward pass
• ✅ Cycle detection in computation graph
• ✅ Numerical stability in loss functions (e.g., LogSumExp for CrossEntropy)

Current Limits:

• Max gradient magnitude: User-configurable clipping
• Max computation graph depth: 10,000 operations
• Tape size limit: Configurable via backend

Risk: ML operations can consume excessive memory or CPU resources.

Attack Vectors:

• Massive tensor allocations (memory exhaustion)
• Deeply nested computation graphs (stack overflow)
• Infinite training loops (CPU exhaustion)
• Large batch sizes causing OOM

Mitigation:

• Memory allocation limits enforced by OS
• Computation graph depth limits
• User-controlled batch sizes
• Gradient checkpointing for large models (planned)

User Best Practices:

// ✅ Validate tensor sizes
totalSize := 1
for _, dim := range shape {
    totalSize *= dim
}
if totalSize > maxAllowedElements {
    return errors.New("tensor too large")
}

// ✅ Limit batch sizes
if batchSize > maxBatchSize {
    return errors.New("batch size too large")
}

// ✅ Use gradient accumulation for large batches
for i := 0; i < numMicroBatches; i++ {
    loss := model.Forward(microBatch[i])
    grads := backend.Backward(loss)
    accumulateGradients(grads)
}
optimizer.StepWithAccumulated(accumulatedGrads)

Risk: Large dimensions can cause integer overflow when computing strides or total sizes.

Example Attack:

Shape: [1000000, 1000000, 1000]
Total size: 1e15 elements (overflows int64)
Result: Small buffer allocated, large data read → crash

Mitigation:

• All shape calculations checked for overflow
• Safe multiplication with overflow detection
• Maximum dimension size: 2^31-1 per dimension
• Maximum total elements: 2^50 (~1 petabyte)

Current Limits:

• Max dimension value: 2^31-1
• Max total tensor elements: 2^50
• Max tensor size: Limited by available memory

Risk: Malicious training data can poison model weights.

User Responsibility:

• Validate training data sources
• Implement data augmentation carefully
• Monitor training metrics for anomalies
• Use differential privacy if needed

Framework Support:

• ✅ Gradient clipping to limit poisoning impact
• 🔄 Differential privacy utilities (planned for v0.2.0)
• 🔄 Robust loss functions (planned)

Security Advantage: Born ML Framework has zero external dependencies in the core library.

Benefits:

• ✅ No supply chain attacks via dependencies
• ✅ Complete control over code security
• ✅ No hidden vulnerabilities from third-party code
• ✅ Pure Go implementation (memory-safe language)

Testing Dependencies (dev only):

• No runtime dependencies
• Standard library only

Always validate untrusted inputs:

// Validate input shapes
func validateInput(input *tensor.Tensor, expectedShape tensor.Shape) error {
    if !input.Shape().Equal(expectedShape) {
        return fmt.Errorf("invalid shape: got %v, expected %v",
            input.Shape(), expectedShape)
    }
    return nil
}

// Validate numerical stability
func validateTensor(t *tensor.Tensor) error {
    data := t.Raw().AsFloat32()
    for _, v := range data {
        if math.IsNaN(float64(v)) || math.IsInf(float64(v), 0) {
            return errors.New("tensor contains NaN or Inf")
        }
    }
    return nil
}

Set limits for untrusted operations:

// Limit tensor sizes
const maxTensorSize = 1e9 // 1 billion elements

if tensorSize(shape) > maxTensorSize {
    return errors.New("tensor too large")
}

// Limit batch sizes
const maxBatchSize = 1024

if batchSize > maxBatchSize {
    return errors.New("batch size too large")
}

Always check errors - failures may indicate attacks:

// ❌ BAD - Ignoring errors
model := born.NewLinear(784, 10, backend)
output := model.Forward(input)

// ✅ GOOD - Proper error handling
model := born.NewLinear(784, 10, backend)
if err := validateInput(input, model.ExpectedShape()); err != nil {
    return fmt.Errorf("input validation failed: %w", err)
}

output := model.Forward(input)
if err := validateTensor(output); err != nil {
    return fmt.Errorf("output validation failed: %w", err)
}

Status: Active mitigation via stable algorithms.

Risk Level: Medium

Description: Floating-point operations can lose precision or produce NaN/Inf values.

Mitigation:

• Numerically stable loss functions (LogSumExp for CrossEntropy)
• Gradient clipping support
• NaN/Inf detection in critical paths

Status: Inherent from Go's memory safety.

Risk Level: Low

Description: Go provides memory safety, but unsafe operations exist.

Mitigation:

• No unsafe package usage in core tensor operations
• Bounds checking on all array accesses
• Panic recovery for critical operations

Status: Race detector enabled in CI.

Risk Level: Low

Description: Concurrent access to shared tensors can cause data races.

Mitigation:

• Thread-safe backend operations
• Race detector in CI pipeline
• Documentation of thread-safety guarantees

• ✅ Unit tests with edge cases (NaN, Inf, overflow)
• ✅ Integration tests with realistic models
• ✅ Race detector in CI (-race flag)
• ✅ golangci-lint with 34+ security-focused linters
• ✅ Bounds checking in all tensor operations

• 🔄 Fuzzing with go-fuzz
• 🔄 Property-based testing with gopter
• 🔄 Static analysis with gosec
• 🔄 SAST scanning in CI
• 🔄 Adversarial robustness testing

No security vulnerabilities have been reported or fixed yet (project is in initial release v0.1.0).

When vulnerabilities are addressed, they will be listed here with:

• CVE ID (if assigned)
• Affected versions
• Fixed in version
• Severity (Critical/High/Medium/Low)
• Credit to reporter

• GitHub Security Advisory: https://github.com/born-ml/born/security/advisories/new
• Public Issues (for non-sensitive bugs): https://github.com/born-ml/born/issues
• Discussions: https://github.com/born-ml/born/discussions

Born ML Framework does not currently have a bug bounty program. We rely on responsible disclosure from the security community.

If you report a valid security vulnerability:

• ✅ Public credit in security advisory (if desired)
• ✅ Acknowledgment in CHANGELOG
• ✅ Recognition in README contributors section
• ✅ Priority review and quick fix

Thank you for helping keep Born ML Framework secure! 🔒

Security is a continuous process. We improve our security posture with each release.