LangSec slides.
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Justin Hsu
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author: Topics in Security and Privacy Technologies (CS 839)
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title: Language-Based Security
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date: November 21, 2018
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---
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# LangSec: Principles
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## Programs are written in programming languages
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## Security holes are bugs
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1. Programmer writes some code
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2. Programmer makes a mistake!
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- Forgot to check permissions
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- Mixed private and public data
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- Didn't allocate enough space
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- ...
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3. Attacker exploits the security flaw
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## Programming languages: <br> first line of defense
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- Catching errors earlier is better
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- Earliest possible time: when program is written
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- Easier to reject program than try to protect against it
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## Design languages <br> to reduce security flaws
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- Make it easier for programmer to do right thing
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- Make certain kinds of bugs impossible
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- Limit damage caused by any bugs
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## When are errors caught?
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- When the program is running
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- Stop program when it does something unsafe
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- "Dynamic analysis"
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- When the program is compiled
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- Reject bad program before it even runs
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- "Static analysis"
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# Overall Strategy
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## 1. Pick a language
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- Real languages: Java, C, ...
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- Highly complex: tons of features
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- Hard to modify language
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- Idealized "core" languages
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- Much simpler, small number of features
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- Model "essence" of real languages
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## 2. Formalize what programs "do"
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- Usually: run it on a machine and find out!
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- Not very useful for proofs...
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- Formalize behavior mathematically, on paper
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- Discard "unimportant" details
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- Describe how program "steps"
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## 3. Describe how to check <br> a given program
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- Must work *without* running the program
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- Other desirable features:
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- Scales up to large programs
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- Runs in a reasonable amount of time
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- Doesn't reject too many correct programs
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## 4. Prove correctness
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- We want to prove two things:
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- Soundness: catch all buggy programs
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- Completeness: accept all correct programs
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> Almost always: can't have both!
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## Usually: pick soundness
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- All buggy programs are rejected
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- If the check says "safe", then it is safe
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- But: some safe programs might be rejected
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- Hopefully, not too many
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# Imperative Languages
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## Most familiar
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- Basis of many popular languages
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- Java, C++, Python, ...
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- Program executes sequence of instructions
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- Can read/write to *variables*
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## Keep essential features
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- Assignments to variables
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- Sequencing ("semicolon")
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- Conditionals ("if-then-else")
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- Loops
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## Drop fancier features
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- Memory management
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- Jumps and gotos
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- Function pointers
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- Templates
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- ...
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## Example
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# Functional Languages
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## Maybe a bit less familiar
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- No imperative features
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- Can't modify variables
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- Usually no looping command (instead: recursion)
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- Instead: functions
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- Define functions
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- Call ("apply") functions on arguments
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## Simpler to formalize
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- Program includes "all the information"
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- Behavior doesn't depend on state of variables
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- Program "runs" by changing the code itself
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- Simplifies all the way down to final answer
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## Example
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# Operational Semantics
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## Programs execute by "stepping"
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- Start with program
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- Imperative: plus variable setting
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- In each step, perform update:
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- Functional: modify the program
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- Imperative: update variables
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- Terminates when it stops stepping (is a "value")
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## Example
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## Different styles
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- Big-step
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- Describe value a program eventually steps to
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- Small-step
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- Describe one step of a program
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# Type Systems
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## Assign "types" to programs
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- A type $\tau$ describes a class of programs
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- Usually: well-behaved in some way
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- Can automatically check if program has type $\tau$
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- Type of program depends on types of its pieces
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- Scales to large programs
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## Example
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## Strengths
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- Lightweight
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- Checking types is simple, automatic
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- Don't need to run program
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- Intuitive meaning
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- Can't add a String to a Boolean
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- Programmers often think in terms of types
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- Identify correct programs
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## Weaknesses
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- Programmer may need to add some type hints
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- Extra annotations in program
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- Common for more complex types
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- Compiler sometimes rejects correct programs
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- Figuring out why can be very frustrating
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- May need to write program in more awkward form
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## Types can be complex
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- Simpler types
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- String, Char, Bool, Int, function types, ...
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- More complex types
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- Secret values/public values
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- Trusted values/untrusted values
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- Local data/remote data
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- Random values
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- ...
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## Prove: soundness
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- "Soundness theorem"
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- Relate type system to operational behavior
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- If program has a type, it should be well-behaved
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- Many possible notions of "well-behaved"
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- Don't add Strings to Bools
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- Don't mix public and private data
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- Don't write past end of buffer
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- ...
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> "Well-typed programs can't go wrong"
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@ -33,7 +33,7 @@ Data Privacy* (AFDP) by Cynthia Dwork and Aaron Roth, available
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11/14 | Verifiable differential privacy <br> **Paper:** Narayan, Feldman, Papadimitriou, and Haeberlen. [*Verifiable Differential Privacy*](https://www.cis.upenn.edu/~ahae/papers/verdp-eurosys2015.pdf). <br> <center> <h5> **Due: Milestone 2** </h5> </center> | Fayi
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11/19 | Homomorphic encryption <br> **Paper:** Halevi and Shoup. [*Algorithms in HElib*](https://www.shoup.net/papers/helib.pdf). | Yue
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| <center> <h4> **Language-Based Security** </h4> </center> |
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11/21 | Language-based security: overview and basics | Justin
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11/21 | [Language-based security: overview and basics](../resources/slides/lecture-langsec.html) | Justin
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11/26 | Languages for privacy <br> **Paper:** Reed and Pierce. [*Distance Makes the Types Grow Stronger: A Calculus for Differential Privacy*](https://www.cis.upenn.edu/~bcpierce/papers/dp.pdf). | Sam
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11/28 | Bonus lecture on applied crypto <br> <center> **GUEST LECTURE** </center> | Somesh Jha
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12/3 | Languages for authenticated datastructures <br> **Paper:** Miller, Hicks, Katz, and Shi. [*Authenticated Data Structures, Generically*](https://www.cs.umd.edu/~mwh/papers/gpads.pdf). | Zichuan
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